Energy and Extractives Global Practice Group South Asia Region (SAR) Energy and Extractives Global Practice Group South Asia Region (SAR) Data Analytics for Advanced Metering Infrastructure A Guidance Note for South Asian Power Utilities November 2018 Disclaimer This document has been prepared for the sole purpose of sharing the results of a global AMI survey and resulting insights related to the AMI strategy for India and South Asia. This does not endorse individual vendors, products or services in any manner. Therefore, any reference herein to any vendor, product or services by trade name, trademark, manufacturer or otherwise does not constitute or imply the endorsement, recommendation or approval thereof. Copyright © 2018 The International Bank for Reconstruction and Development THE WORLD BANK GROUP 1818 H Street, N.W. Washington, D.C. 20433, U.S.A. All rights reserved The findings, interpretations and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, or its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility whatsoever for any consequence of their use. The Boundaries, colors, denominations, other information shown on any map in this volume do not imply on the part of the World Bank Group any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. The material in this publication is copyrighted. However, it may be reproduced in whole or in part and in any form for educational or non-profit uses, without special permission provided acknowledgment of the source is made. Requests for permission to reproduce portions for resale or commercial purposes should be sent to the Manager, Energy and Extractives Global Practice (South Asia) at askeex@ worldbankgroup.org. The World Bank encourages dissemination of its work and will normally give permission promptly. The Manager would appreciate receiving a copy of or link to the publication that uses this material for its source sent in care of the address listed. All images remain the sole property of their source and may not be used for any purpose without written permission from the source. Table of Contents Acknowledgements v Abbreviations vi Executive Summary ix 1. Data Analytics in Power Utilities 1 1.1 Overview of Data Analytics 2 1.2 Data Analytics: Scope and Application Points in the Electricity Utility Industry 4 1.3 Meter Data Flow in MBC Process and Analysis System 6 1.4 Core MBC Analytics 8 1.5 Advanced Revenue Cycle Management Analytics 18 1.6 Key Issues/Risks Associated with Data Analytics 22 1.7 Benefits of AMI over AMR for Improved Data Analytics 23 2. Procurement and Deployment of Analytics Systems 28 2.1 Data Analytics System Architecture 28 2.2 Approaches for Procurement and Implementation of Analytics Systems 31 2.3 Technical Specification for Analytics Systems 34 2.4 Functional Requirements of Advanced Analytics System 36 2.5 Sizing and Typical Bill of Quantity for Analytics System 38 References and Links 40 Appendices 41 Appendix A. Description of Key Technology Terms 41 Appendix B. Meter Reading Quality Checks (RQCs) – 2nd Level: Screenshots 43 Appendix C. Billing Quality Checks (BQCs) – 3rd Level: Screenshots 50 Appendix D. Meter Data Exception-Generation Checks: Screenshots 56 Appendix E. MIS Reports: Screenshots 75 Appendix F. AMR Data Analytics – Use Cases: Screenshots 89 Appendix G. Billing and Collection Data Analytics Use Cases – Screenshots 98 Appendix H. Threshold Values for Exception Generation 106 Table of Contents  iii Tables Table 1: Analytics Application Areas and Processes 4 Table 2: List of On-Site Meter Read Checks using MRD 9 Table 3: List of RQCs 9 Table 4: List of BQCs 10 Table 5: List of Meter Data Exception Generation Checks 11 Table 6: Illustrative frequency of Meter Data Exception Check 14 Table 7: MIS Reports 15 Table 8: List of Use Cases Based on AMR Data Analytics 19 Table 9: List of Use Cases based on Billing and Collection Data Analytics 21 Table 10: Key issues and solutions associated with data analytics 22 Table 11: Comparison of Functionality of AMR and AMI Meters 24 Table 12: Details of Various Components of Analytics System 29 Table 13: Technology Solutions in Use in Various Utilities for Analytics Systems 30 Table 14: Utility Transition Stages in Adopting Analytics Systems 31 Table 15: Comparison of Procurement Models 34 Table 16: Hardware and Software Specification of Analytics System 35 Table 17: Comparison of Cloud-based and Physical Database Solutions 38 Table 18: Sizing and Typical Bill of Quantity for Analytics System 38 Table 19: Threshold Values for Exception Generation for Single-Phase Meters 106 Table 20: Threshold Values for Exception Generation for Three-Phase Meters 108 Figures Figure 1: Stages of Analytics 3 Figure 2: MBC Process Flow Diagram for AMI and Non-AMI Scenario 6 Figure 3: Architectural Diagram of Billing and Data Analysis System 7 Figure 4: Detailed Meter Data Flow Diagram for Exception Generation 8 Figure 5: Sample Exception Generation Check Output 14 Figure 6: Data Analytics System Conceptual Architecture 28 Figure 7: Transition Stages for South Asian Utilities 31 Figure 8: Consolidated Procurement Model 32 Figure 9: Component-wise Procurement Model 33 Figure 10: Software-as-a-Service Procurement Model 34 iv  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Acknowledgements This report was prepared by a World Bank team comprising Gailius J. Draugelis, Lead Energy Specialist (co-Team Lead); Rohit Mittal, Senior Energy Specialist (co-Team Lead); Amol Gupta, Energy Specialist (co-Team Lead); Phillip Matthew Hannam, Energy Economist; and Neetu Sharda, Program Assistant. The team is grateful to World Bank Group colleagues who peer reviewed the report: Kwawu Mensan Gaba, Lead Energy Specialist/Global Lead, Power Systems, World Bank; Kelli Joseph, Senior Energy Specialist, World Bank; and Peter Mockel, Principal Industry Specialist, International Finance Corporation. The team is also most grateful to Chris Marquardt for his invaluable editing of the final report. Demetrios Papathanasiou, Practice Manager, Energy and Extractives Global Practice (South Asia unit), also provided much appreciated guidance and advice for this project. The report is prepared based on background study undertaken by Deloitte Touche Tohmatsu India LLP team comprising James Thomson, Principal, US (Project Director), Michael Danziger, Managing Director, US (Team Leader);  Anujesh Dwivedi, Partner, India (Team Leader);  Ajay Madwesh, Senior Manager, US; Pankaj Kumar Goinka, Senior Manager, India; Peter Schmidt, Manager, US; Kyle Webb, Senior Manager, US; Rohit Deshpande, Specialist, US; Joel Abraham, Consultant, India; which partnered with Tata Power Delhi Distribution Limited (TPDDL) specifically on aspects of metering, billing and collections analytics use cases. TPDDL team was represented by Sandeep Dhamija, Deputy General Manager.  The Bank team sincerely appreciates the hard work, dedication and collaborative spirit of the consulting team. Company surveys that were carried out in the spring of 2018 and technical report preparation were carried out by Deloitte Touche Tohmatsu under the guidance of the World Bank team.  Findings of the survey and analysis were shared at a workshop in New Delhi on June 8, 2018 with participants from Bangladesh, Bhutan, India, and Nepal. The report was also informed by an industry stakeholder consultation in Jaipur, November 2017. The Bank team is deeply thankful to all workshop and consultation participants for sharing their invaluable insights and comments. The funding for this report was provided by United Kingdom’s Department for International Development through the World Bank-managed Trust Fund programs – the South Asia Regional Trade and Integration Program and the Program for Asia Connectivity and Trade – and the World Bank. Acknowledgements  v Abbreviations AMI Advanced Metering Infrastructure ADMS Advanced Distribution Management System AMR Automatic Meter Reading API Application Programming Interface AT&C losses Aggregate Technical and Commercial Losses BCS Base Computer Software BI Business Intelligence BOM Bill of Materials BoQ Bill of Quantity BOOT Build, Own, Operate and Transfer BOT Build, Own and Transfer BQC Billing Quality Checks CAPEX Capital Expenditure CDW Corporate Data Warehouse CEA Central Electricity Authority CMMI Capability Maturity Model Integration ComEd Commonwealth Edison COTS Commercial Off-The-Shelf (Software) CPU Central Processing Unit CRM Customer Relationship Management CT Current Transformer DCU Data Concentrator Unit DISCOM Distribution Company DLMS Device Language Message Specification DMS Distribution Management System DTR/DT Distribution Transformer EDA Exploratory Data Analysis EESL Energy Efficiency Services Limited ERP Enterprise Resource Planning ESD Electrostatic Discharge ETL tool Extract, Transform and Load Tool Gbps Gigabits Per Second GHz Gigahertz GIS Geographic Information System GPRS/GSM General Packet Radio Service/Global System for Mobile Communications vi  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities HBA Host Bus Adapter HDD Hard Disk Drive HES Head-End System HHU Hand-Held Units HT/LT High-Tension/Low-Tension IBM International Business Machines Corporation Ib Base Current IEEE The Institute of Electrical and Electronics Engineers IoT Internet of Things IP Internet Protocol IPDS Integrated Power Development Scheme IS Indian Standard IT Information Technology JDBC Java Database Connectivity kV kiloVolt kVARh kiloVolt-Amperes Reactive hours kWh kiloWatt-hour lasso Least Absolute Shrinkage and Selection Operator (Regression Analysis Method) LDAP/AD Lightweight Directory Access Protocol/Active Directory mA milliAmpere MAF Manufacturer’s Authorization Form MBC Metering, Billing and Collection MDI Maximum Demand Indicator MDMS Meter Data Management System MIS Management Information System MoP Ministry of Power, Government of India MRD Meter Reading Device MRI Meter Reading Instrument mT milliTesla MTD Month to Date NIC Network Interface Card NLP Natural Language Processing NMS Network Management System NVM Nonvolatile Memory OBIEE Oracle Business Intelligence, Enterprise Edition OAC Office Automation Consultants OEM Original Equipment Manufacturer OGC Open Geospatial Consortium OMS Order Management System OT Operational Technology Abbreviations  vii PF Power Factor PFA Predictive Failure Analysis PoD Proof of Delivery PPA Power Purchase Agreement PSU Public Sector Undertaking RAPDRP Restructured Accelerated Power Development and Reforms Program RDBMS Relational Database Management System RF Radio Frequency RFP Request for Proposals RQC Reading Quality Checks RTC Real-Time Clock SaaS Software as a Service SAP BW Sap Business Warehouse (Software) SAP HANA Sap High Performance Analytic Appliance (Software) SAP IS-U Sap’s Industry-Specific Solution for the Utilities Industry SAP PAL Sap Predictive Analysis Library (Part of Sap Hana) SCADA Supervisory Control and Data Acquisition SAIDI System Average Interruption Duration Index SI Systems Integrator SLA Service Level Agreement SPSS Statistical Package for the Social Sciences SQL Structured Query Language SSD Solid-State Drive SSH Secure Shell T Tesla TB Terabyte ToD Time of Day ToU Time of Use TPDDL Tata Power Delhi Distribution Limited UDAY Ujwal Discom Assurance Yojana UoM Unit of Measurement UPF Unity Power Factor VAR Volt-Ampere Reactive WIMS Work Information Management System XML Extensible Markup Language YTD Year to Date viii  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Executive Summary The power industry in South Asia1 is on the cusp of a transformation driven by technological advances, decreasing energy intensity, heightened environmental awareness, and evolving customer expectations. Power distribution utilities in these countries are facing various challenges including high levels of Aggregate Technical and Commercial (AT&C) losses, increased energy theft, poor customer services and operational transparency, inefficient load management, and unreliable power supply. Governments in most South Asian countries are helping utilities by implementing various schemes to improve their power sectors. In particular, the widespread and successful adoption of smart metering in advanced economies over the last decade has encouraged South Asian policy makers to take an increasing interest in smart metering systems in hope that they can address some of the chronic issues. Now, with high-level policies in place and utilities keen to adopt smart metering, funding requirement and implementation challenges remain the bottlenecks to mass deployment. A recent World Bank–funded study, Advanced Metering Infrastructure and Analytics Guidance Study for South Asian Utilities, carried out in 2018, developed guidance, based on user experience, on the deployment and operation of Advanced Metering Infrastructure (AMI) and analytics systems by electricity distribution utilities in India and other South Asian countries. The guidance is intended for the ready reference of policy makers and utility managers. The study was divided into two reports. The Survey of International Experience in Advanced Metering Infrastructure and its Implementation, published separately, covers international best practices regarding the end-to-end deployment of AMI systems, including such areas as main functions, procurement options, and the organizational or functional changes needed to implement AMI-enabled business processes. The present report, Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities, is structured as a guidance note to assist utility managers in taking up data analytics systems to realize the full potential of AMI. The report has two main parts, as follows: 1 The World Bank’s South Asia region comprises Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. See https://www.worldbank.org/en/region/sar/overview. Executive Summary  ix Chapter 1 provides an overview of the use of data analytics by power utilities, including conceptual architecture for system deployment, meter data flow in Metering, Billing and Collection (MBC) processes, and data analytics systems. The chapter also covers in detail the core MBC analytics on data exception checks, meter reading, and billing quality checks. Further, various use cases related to advanced analytics for revenue management are also elaborated. The discussion of risks and other issues associated with data analytics systems– including the benefits of AMI over Automatic Meter Reading (AMR) for improved data analytics – will be particularly insightful to utilities that have already initiated or are considering an implementation in the near future. Chapter 2 describes the transition phase utilities go through while adopting data analytics systems, including an explanation of procurement and implementation models for data analytics systems. With a focus on the South Asian context, the chapter outlines the basic technical and functional specifications of analytics systems, sample sizing, and typical Bill of Quantity (BoQ) for the implementation and qualification requirements of potential system integrators. The report was prepared by AMI and utility specialists from Deloitte as well as Tata Power Delhi Distribution Limited (TPDDL), one of the first utilities in India to adopt AMR and AMI technology. Over the last 15 years, TPDDL has gained expertise in AMR technology and core data analytics, and it has recently begun implementing AMI and advanced data-analytics systems. The World Bank conducted a South Asian stakeholder consultation on AMI implementation issues in India on November 29, 2017, during the International Symposium to Promote Innovation and Research in Energy Efficiency (INSPIRE-2017). These stakeholders provided validation and guidance on the scope of the study. The findings of the study were shared on June 8, 2018, at a regional workshop on AMI titled “International and Regional Approaches to Implementation and Data Analytics in New Delhi.” Feedback from workshop participants is reflected in the two reports. Both reports were prepared under the guidance of a team from the World Bank’s Energy and Extractives Global Practice that was based in the World Bank’s offices in Washington, D.C., United States, and New Delhi, India. x  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Data Analytics in 1.  Power Utilities Although Information Technology (IT) solutions have benefited core power utility operations for decades, most utilities in South Asian countries have remained weak in using IT to improve operational efficiency. Nevertheless, a handful of utilities in South Asia – especially the privately owned ones – have not only deployed best-in-class IT solutions in their revenue management and network operations, but have also implemented advanced solutions that leverage data to make decision making insightful, fast and more accurate. In India, the use of operational IT by state-owned power utilities has happened on the back of two schemes promulgated by the Government of India (GoI) – the Restructured Accelerated Power Development and Reforms Program (RAPDRP) and Integrated Power Development Scheme (IPDS)– which introduced IT solutions in core utility operations throughout the country. In November 2015, the GoI launched the Ujwal DISCOM Assurance Yojana (UDAY) program, which seeks to spur the financial turnaround and revival of power distribution companies (DISCOMs) by improving their operational and financial efficiency. As part of the program, participating DISCOMs must install smart meters for all customers consuming more than 200 kilowatt-hours (kWh) per month by December 2019. In this context, Energy Efficiency Services Limited (EESL)2 has undertaken the procurement process for five million smart meters for the states of Haryana and Uttar Pradesh. The key feature of EESL’s procurement approach is build-own-operate-transfer (BOOT) – that is, a pay-as-you-benefit approach. This large, centralized procurement initiative has yielded aggressive pricing by bidders, thereby reducing the cost of smart meters for power utilities. This trend has encouraged several other utilities in other South Asian countries to go beyond pilots and initiate sizeable smart-meter deployment programs. 2 EESL is a “super” Energy Service Company (ESCO) that acts as the resource center for capacity building for DISCOMs, Energy Regulatory Commissions (ERCs), State Development Authorities (SDAs), financial institutions, ESCOs, and so on. Founded in 2010 by the GoI, EESL is currently implementing the largest energy efficiency portfolio in the world. 1. Data Analytics in Power Utilities  1 The implementation of smart metering technology will create a platform for end-to-end “situational awareness”– a true understanding of the full situation – in power utility operations and is considered to be a key milestone in the digital transformation of utilities. 1.1 Overview of Data Analytics While most standardized utility solutions have built-in Management Information System (MIS) modules, the recent proliferation of utility operations systems involving advanced IT penetration and automation means utilities must now consider a holistic approach towards data analytics. In other words, they must now explore ways to derive maximum business advantage from aggregated and real-time data synthesis and trend visualization through advanced data analytics. The Smart Grid concept and its constituent technologies have added new dimensions to electricity distribution systems by providing higher-resolution data for enhanced network operations. Advanced Metering Infrastructure (AMI), which includes smart metering, is considered the key to enabling active/proactive decision making in utility operations– replacing the existing, largely passive mode of operations. By enabling automation at the last node of a utility’s network, smart meters serve as a gateway between a utility and its customers. It serves as more than just a cash box for the utility, however. In addition to helping utilities safeguard their revenue interests while helping customers optimize their consumption, smart meters provide a host of advantages in network operations by boosting utility managers’ situational awareness of conditions along the “last mile” (that is, the final leg of delivery to retail end-users, or customers). Using the “big data” generated by smart meters and systems based on the Internet of Things (IoT), utilities will have a vast array of opportunities to improve operational and commercial efficiency using insights gained from data analytics that may not have been previously feasible. Currently, most utilities in South Asia are focused on addressing growing consumer demand and the related issues of access, availability, quality and affordability of power supply. They are just beginning to explore data analytics solutions that will eventually generate tremendous value in terms of efficiency improvements, enhance customer services and improve financial performance. To unlock the full value of AMI, each utility will need to implement a robust advanced analytics system capable of processing data beyond basic meter-to-cash functionality. To do this, it will first need to determine its business needs and establish a strategy. Once its strategy is aligned with its business architecture, the utility can decide on the types of analysis it intends to undertake and the benefits that can be derived. Data analytics can be broadly grouped into four stages, as shown in Figure 1.  Descriptive analytics helps in understanding what has happened in the past. Examples include meter ageing analysis; determining the “percentage of average billing” trend across months; trends of arrears outstanding across months, consumer categories, and so on; network optimization/load balancing; interconnected feeder analysis; predicting potential defaulting consumers; and theft analytics. 2  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Figure 1:  Stages of Analytics Difficulty in Implementation Descriptive Diagnostic Predictive Prescriptive Analytics Analytics Analytics Analytics • What is • Why did it • What is likely • What should happening? happen? to happen? be done about it? Value Delivered  Diagnostic analytics helps in understanding what has led to success or failure in the past. Examples include sanctioned load violation analysis, network failure analysis, and theft analytics.  Predictive analytics helps in determining the likelihood of occurrence of an event in future based on past data patterns. Examples include footfall optimization at payment centers, energy forecasting, predictive maintenance, predictive fault analysis, and network-asset management.  Prescriptive analytics helps in identifying steps to be taken in future to meet the desired objectives. Examples include energy optimization and Volt-Ampere Reactive (VAR) control, call center efficiency, meter reading, and billing complaints analysis. By its very nature, development of analytics is complex since it aims at integrating structured, unstructured, and time-series data, then aligning the data with system-generated events and alarms. Within a utility context, analytics can be defined as the process of converting data from smart grid sensors and devices by integrating it with a variety of related data sets (including data from operational, non-operational and external systems) to develop models that predict and/or prescribe the next best action – thus creating deep situational awareness. A utility’s external data sources may be based on data that is structured (cellular network data, for example), unstructured (social media feeds and customer questions, for example), part of a time series (outside temperature, for example) or generated by an event (lightning, for example). Integrating the utility’s data and information with these external data sources leads to a deeper understanding of the causes of issues affecting operational, customer and business performance. Using AMI-type machine-to-machine communications, consumer-facing web portals, in-home devices, visualization tools, modelling software, and even spreadsheets results can then give utility managers an enhanced “situational awareness” – that is, a more thorough and precise understanding of operations. Analytics built on data from smart meters is valuable in a variety of ways to a utility. The utility landscape surrounding, and business cases for, adopting AMI analytics vary significantly by region, as well as by utility ownership structure. This section outlines a broad set of use cases that are now possible using AMI data. This section also focuses on the “state-of-the-art” as well as the “art of the possible” to show how utilities can harness AMI data – using both predictive and prescriptive analytical models – not only to realize greater value, but also to shift to the new utility model that regulators are pushing towards. 1. Data Analytics in Power Utilities  3 Data Analytics: Scope and Application Points in the 1.2  Electricity Utility Industry The scope of use cases for AMI analytics is built on data and information generated from core AMI systems or AMI applications, such as an AMI Head-End3System (HES) and Meter Data Management (MDM) systems. Advanced analytics based on data from these systems are intended to boost situational awareness by using predictive and/or prescriptive models to tap into streams of business value. For example, as part of the development of optimal demand dispatch in a demand/response application, the need for accuracy has led to the development of sophisticated models for demand segmentation, demand forecasting and customer segmentation. The specific drivers for the development of analytical models from AMI vary based on geography, regulations and underlying demographics. When compared with the cost of implementing AMI itself, the cost of implementing analytics is quite low and typically has a more immediate payback. The successes in implementing analytics by industry leading utilities using data from AMI systems have had a cascading effect in that they have helped other utilities – especially smaller and mid-market utilities, which tend to be more risk-averse – justify their own accelerated AMI deployments. Globally, many early adopters of AMI went through multiple cycles of integrations, data management strategies, data storage, and tools due to non-standard architectures and solutions. The resulting refinement of architectures and protocols, lessons on effective use of advanced analytics, and development of successful business cases will be particularly critical in regions like Latin America and Asia, where operational savings by themselves may not justify the implementation costs of AMI. Analytics can be useful in taking more informed decisions and actions, whether or not a human is in the loop. For example, protection and control systems that require sub-second responses will be machine- automated, whereas dashboards and reports leveraging business data may operate in a workflow that involves humans making or approving recommended decisions or actions. Table 1 summarizes the main areas in which data analytics has been applied in the electricity utility industry. Table 1: Analytics Application Areas and Processes Application Areas Analytics Process Net metering, sub- Granular consumption data allows utilities to map energy flows on the laterals (i.e. local power metering and other lines) and from the laterals to the aggregation points based on their network models. This complex metering enables utilities to bill municipalities and water companies accurately for street lighting and scenarios water pump operations – operations that were previously monitored and billed separately. Feeder capacity and Consumption, voltage and power-factor data from each consumption point allows utilities performance to analyze key power parameters downline. In feeders with reliability issues or with high penetration of distributed energy resources, or DER (smaller sources of variable power connected to the grid but widely distributed geographically), a very granular analysis of 3 A Head-End System (HES) is hardware and software that receives meter data sent to the utility. Head-end systems may perform a limited amount of data validation before either making the data available for other systems to request or pushing the data out to other systems. 4  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Application Areas Analytics Process the feeder’s capacity and reliability can be performed. The ability to analyze voltage sags and swells at an individual consumption point allows a utility to address capacity or design issues. Smart meter data is immensely valuable in assessing the load on the transformer (peak and nominal), which allows the utility to plan transformer upgrades and replacement programs to minimize outages before they occur. Additionally, capacity and network constraints at the feeder and lateral levels are assessed to a highly granular degree. Advanced outage With the smart meter “last gasp” capabilities, along with the logged events, outage analysis analysis and prediction become significantly more accurate. (“Last gasp” refers to a short transmission a smart meter makes when it loses power, typically to signal the power loss and often to specify the time and date of the outage.) When a major weather event is expected, analyzing the potential extent of the outage, as well as possible nested outages, becomes a highly effective tool for the utility. This helps maintain a low System Average Interruption Duration Index (SAIDI) and serves customers by restoring electricity faster following an outage. Demand segmentation Total demand calculated based on consumption can be analyzed by disaggregating the demand into multiple cross-sections or segments. The interaction of these segments at different prices, loads, temperatures, etc. over time improves the ability to predict demand. Advanced unit In situations where the wholesale markets allow smaller renewable and storage-based commitment models generation, and where demand aggregators bid into the Capacity and Day-Ahead markets, the ability to track the performance of these generation sources relative to demand becomes critical. For example, to address balancing and security constraints while following regulatory mandates on renewable generation, grid operators use granular demand data to predict shortfalls ahead of time and avoid buying generation through the costlier real-time and ancillary markets. To generate near-real-time and predictive demand curves, grid operators use historical and smart-meter data. Power quality Utilities have long sought conservative voltage reduction and improved management of voltage profiles down to the level of feeder lines. With the smart meters capturing voltage profiles, voltage sags and voltage swells at the consumer level, feeder performance – including feeder quality and intermittent outages – can be captured with high fidelity. In instances where the voltages can be managed to the lower limits specified by the reliability regulator through operating the tap changers at the substation, a utility can optimize generation. Technical and non- Distribution feeders can be sectioned to increase greater reliability and feeder reconfiguration technical loss analysis by using AMI data to measure the technical and non-technical losses more accurately. The AMI data provides a true measure of the total losses in the distribution feeder and guidance to target diversion and energy theft. Energy theft models are then used for revenue assurance. Advanced switching As the distribution feeders are sectioned to increase greater reliability and for feeder reconfiguration, AMI data can be used to confirm that switching operations have occurred correctly, as well to validate the network model. Asset performance Using data from Supervisory Control and Data Acquisition (SCADA) systems, utilities can analyze the performance of network assets at the substation level, and sometimes at the feeder level. With AMI data, this analysis can be conducted along the “last mile”. Customer preferences, As customers become active participants in the energy equation, understanding precisely customer propensity what drives their behavior becomes critical. AMI data facilitates this. Understanding the and incentives model consumption patterns of each individual consumer and comparing them to similar consumers (within the applicable regulatory boundaries) allows for the analysis of the preferences and patterns of a class of consumers. 1. Data Analytics in Power Utilities  5 1.3 Meter Data Flow in MBC Process and Analysis System The initial step in the Metering, Billing and Collection (MBC) process is the planning and scheduling of the meter-reading cycle and walking sequence. There are two scenarios. In the Advanced Metering Infrastructure (AMI) Scenario, the meter data is directly received at the HES4 if the meter is coupled with a data collector, with the remote meter data transfer taking place through wired or wireless communication. In the Non-AMI Scenario the meter reader carries a hand-held Meter-Reading Device (MRD) in the field to record meter data and run some primary validations on the meter data based on the checks built into the device. The data is then uploaded to the server. Figure 2 describes the process of meter reading and billing in both AMI and Non-AMI Scenarios. In both scenarios, the following further checks are done to ensure that the bill delivered to the consumers is correct in all respects:  Meter Reading Device (MRD) on-site meter-read check: Here a primary check is performed to ensure that the meter reader reads the correct meters and provides correct information. This check is performed only in the non-AMI scenario, where manual meter reading is done monthly. Figure 2: MBC Process Flow Diagram for AMI and Non-AMI Scenario Meter Reading Data Smart Meter with moved to Billing Server BCS / HES Server Communication Module SAP SAP Planning & Data Downloading in Base Meter Reading Reading Data uploaded Scheduling MRD from Billing Server through MRD: in Billing Server 1st Level Check Reading Quality Check: Bill Generation Exception handling & Trouble 2nd Level Check shooting (Follow up meter reading) AMI Scenario Billing Quality Check: Printing & Invoicing Bill Distribution Non AMI Scenario 3rd Level Check 4 The HES is sometimes also referred as the Meter Data Acquisition System (MDAS) in the case of AMR. 6  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Figure 3: Architectural Diagram of Billing and Data Analysis System Manual Process Automatic Process Comm. Modems .dst Application Server Database format Full / Analysis Data Vendor specific data format APIs CDF Billing Engine Vendor Billing Data 1 RS 232/ Billing Data Vendor 1 Meters Optical Port Database Server Full / Analysis Data Comm. Modems .rr3/.rb 3 format Data Analytics Vendor APIs Application 2 Optical / Magnetic RS 232/ Data Storage Optical Port Devices (CD/ DVD) Vendor 2 Meters Comm. Modems Analysis .cmd/.cdf MIS Reports format Team Vendor 3 APIs RS 232/ Optical Port Vendor 3 Meters Note: API = application programming interface.  Reading Quality Check (RQC): This ensures that the meter data captured are accurate prior to billing in terms of consumption.  Billing Quality Check (BQC): This ensures the quality and correctness of bills in terms of amount. Further meter data exception-generation checks are performed on the full sets of meter data in both AMI and Non-AMI scenarios based on the frequency of meter reading and the utility’s business requirements. A typical technical architectural diagram showing meter data flow for billing and analytics is shown in Figure 3. The diagram depicts meter data flow from smart meters to the billing engine and the data analytics application servers. A DISCOM may have meters from different vendors deployed at the consumer premises. The interoperability of meters from different vendors with HES system is an issue for AMI/AMR deployment. This is because each of the various vendors uses its own proprietary protocols for meter data exchange with HES system. Although the Bureau of Indian Standards has formulated the standards for smart metering (IS 16444), proprietary protocols remain in use for AMR meter data exchange from different vendors. To resolve this issue, the DISCOMs need to have HES/BCS5 software from respective meter vendors in their HES server to poll meters using proprietary Application Programming Interfaces (APIs), as shown above in Figure 3. The meter data received in different formats by the HES/BCS system will be parsed and converted to standard .xml format, which can then be directed to corresponding 5 Head-end system/base computer software. 1. Data Analytics in Power Utilities  7 Figure 4: Detailed Meter Data Flow Diagram for Exception Generation Manual Process Automatic Process Invoicing Billing Billed Billing Data Billing Engine BQC Reads RQC • Hardware NOT OK Problem Billing Data H OK Rectification • Enforcement E Manual Checks NOT OK Team Analysis Data S NOT OK OK Smart Meter General Analysis Engine (Exception Checks) OK Data Base Analysis Data billing and analytics applications. However, this solution has a higher implementation cost. If the standardization of AMI meter data exchange can be mandated for the vendors through a policy-level decision or in the tender documents for procurement by the utility, the cost of implementation can be further reduced. Figure 4 shows a detailed diagram of meter data flow, along with its application at different steps in MBC cycle and data analysis to “generate” (i.e., check for and list) exceptions.6 Smart meters can provide billing reads and full data (billing reads, instantaneous data, event data, load profile, and so on) separately, in line with utility requirements. Billing reads are acquired from meters each month, and analytics is done on the full data set acquired from meters at custom frequencies set by the utility for different consumer segments. After each level of checks, the exception cases are forwarded for manual analysis, which may include on-site checks. Verified data is forwarded to a database for storage and future reference. 1.4 Core MBC Analytics Data received from the smart meter must be processed (i.e. checked for exceptions) before transferring to the billing and analytics engine. There may be multiple levels of data-quality checks in a typical MBC cycle. This section describes checks occurring at four levels: on-site meter readings, comparison of those readings to existing consumer data, checks at the billing stage, and further checks using rule-based analysis. 1.4.1 First Level: On-Site Meter Reading Checks Using MRDs In the non-AMI scenario, meter readers carry a hand-held Meter Reading Device (MRD). At each site, the MRD is used to perform first-level checks (see Table 2) on the data retrieved from the meter to ensure data correctness and to reduce the number of follow-up meter readings required. 6 In programming, exceptions are anomalous or exceptional conditions requiring special processing. 8  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Table 2: List of On-Site Meter Read Checks using MRD First-level (primary) Function Exception Check Reading check Checks if current meter reading is less than previous reading Zero consumption Checks if the meter consumption is absolute zero Drop in consumption Checks if the current consumption is less than the average consumption/threshold value High MDI*/sanctioned load Compares the current-month MDI reading against the sanctioned load and previous month’s reading Meter-unread scenario Mandatory input in each MRD device that captures the reason the meter reading was not performed – such as a locked house or faulty meter * MDI = Maximum Demand Indicator. If these checks yield flags, the meter reader is directed to take a photograph of the meter or to re-read the meter at the site. 1.4.2 Second Level: Reading Quality Checks (RQCs) Reading Quality Checks (RQCs) ensure the quality of meter reading data before billing. Usually limited to the meter data used specifically for billing, RQCs ensure that supply units are measured and billed accurately. The RQCs compare each meter data set to the respective “consumer master data” and historical trends, then generate an exception list for re-reading. The utility can then cross-check the data against the consumer master data – which includes details on consumer type, tariff slab (price based on usage range), meter, and sanctioned load and maximum demand, as well as consumer address and other billing-related information. Table 3 lists the RQCs a utility typically performs before generating bills. Sample illustrative screenshots of RQCs are provided in Appendix B. Table 3: List of RQCs Benefits/Use Function What It Looks for Electrical Parameters Considered Cases Low consumption Checks for variations in the Prevent revenue  Meter data: kWh consumption consumption trend of a particular leakage  Consumer master data: Type of consumer (with reference to low institution and sanctioned load with value thresholds for each category). previous MDIs High Maximum Checks for variations a consumer’s Prevent revenue  Meter data: kW value Demand Indicator MDI trend with reference to his/her leakage  Consumer master data: Type of (MDI) historical meter reading data and institution and sanctioned load with category threshold value. previous MDIs Reading reversed/ Checks for erroneous readings on Prevent revenue  Meter data: kWh and kVAh wrong reading meter energy consumption – such as leakage; verify  Consumer master data: Type of very high or negative consumption. meter data institution and sanctioned load with previous MDIs Low power factor Checks for power factor value below Prevent revenue  Meter data: Power factor the threshold defined by utility. leakage  Consumer master data: Type of institution and sanctioned load with previous MDIs 1. Data Analytics in Power Utilities  9 Benefits/Use Function What It Looks for Electrical Parameters Considered Cases Zero consumption Checks whether previous month Prevent revenue  Meter data: kWh reading and current month kWh readings are leakage  Consumer master data: Type of same, so that resultant per-month institution and sanctioned load with consumption is zero. previous MDIs Contract demand Compares the demand value from Prevent revenue  Meter data: MDI (kW) meter reading data against the leakage  Consumer master data: Type of consumer master database. (There institution and sanctioned load with is a penalty for higher demand previous MDIs compared to contract demand.) Non- Checks to see if: Prevent revenue  Meter data: kWh and kW reprogramming  Tariff slab information from meter leakage; verify  Consumer master data: Type of data does not match consumer data institution and sanctioned load with master data previous MDIs  Meter reset-date info from meter data does not match consumer billing master data  Meter is not programmed to store data in Time-of-Day (ToD) slabs; HES system after receiving data identifies consumer to be billed on ToD 1.4.3 Third Level: Billing Quality Checks (BQCs) BQCs ensure the quality of bills generated for each consumer based on their electricity usage. BQCs are similar to RQCs, but more stringent and accurate in terms of amount billed with respect to billing parameters. They are thus a critical component of any effort by a utility to reduce its commercial losses. Table 4 provides a typical list of BQCs a utility performs before generating bills. Sample illustrative screen shots of BQCs are shown in Appendix C. Table 4: List of BQCs Function What It Looks for Benefits/Use Cases Parameters Considered Current billed  Generatesa trend analysis of current billed  Billing correctness month billed  Current demand demand along with comparison to threshold demand value Inflated bill  Checks for high bill amount compared to  Billing correctness  Billed amount threshold value for the particular category  Consumer satisfaction Negative  Checks if bill amount for the current month is  Revenue assurance  Billed amount amount negative High slab  Slab value = number of billing days/number of  Consumer satisfaction  Number of units during days in a month  Revenue assurance billing period  Comparison of slab value with the threshold  Billing correctness  Tariff defined by the value for particular category of consumer Regulator Solar meter  Checks the correctness of bills for consumers  Billing correctness  Number of units consumed having net metering and number of units fed back into the grid  Billed amount 10  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Further Checks Using Rule-Based Analysis (further meter data exception 1.4.4  generation checks) In addition to the aforementioned checks, utilities can use various other exception-generation checks to identify revenue leakage, theft, and so on. This analysis can be done on a predefined frequency (i.e., at various predefined intervals) depending on the category of consumers. The raw meter data received is subjected to rule-based analysis for identifying anomalies. These checks are oriented toward either consumers or utilities. Consumer-oriented checks generate flags when, for example, monthly bill or energy consumption exceeds the average value range; these checks can be done hourly or daily depending on the resolution of the data received. Utility-oriented checks includes checks on data for revenue assurance, system-condition monitoring, and so on. Data analysis rules could be selectively applied to an individual metering node or groups of metering nodes or to channels common to different metering nodes. A list of exception checks that a utility could follow based on various data received from meter is provided in Table 5. Table 5: List of Meter Data Exception Generation Checks Type of Electrical Consumer No. Exception Function Benefits/Use Cases Parameters Categories Check Considered 1 Consumption Checks that consumption is inline Identification of:  Kilowatt- All categories comparison with historical trend and threshold  Tamper condition hours (kWh) values  Meter failure, under- recording 2 Voltage failure Checks that system voltage  System condition check  Phase All categories values are in line with tolerance of tamper  Identification voltage and threshold values condition 3 Assessed Checks whether the calculated Identification of:  kWh All categories consumption load factor is in the predefined  Tamper condition threshold load-factor range for  Situation where the particular consumer category consumer is not running load for maximum hours, causing a loss to the utility 4 Data corruption Checks the value of maximum Identification of:  kW/kWh All categories demand indicator (MDI)/  Meter failure consumption data/digits recorded  Data downloading against the threshold values issue 5 Non-Volatile Identifies and records meter Identification of:  Meter events All categories Memory7 (NVM) memory failures and raises “NVM  Tamper condition data failure failure event” flags  Meter failure 6 Power failure Checks two threshold values: Identification of:  Phase All categories  Number of power failures: The  System condition voltages cumulative total number of  Tamper condition power failures should be less than the threshold value defined.  Time of power failure: The cumulative power failure duration should be less than threshold value defined. 7 Non-volatile memory (NVM) is a type of computer memory that has the capability to hold saved data even if the power is turned off. 1. Data Analytics in Power Utilities  11 Type of Electrical Consumer No. Exception Function Benefits/Use Cases Parameters Categories Check Considered 7 Neutral Checks for unbalanced neutral- Identification of:  Neutral All categories disturbance current values or voltage values  System condition current – stringent  Tamper condition  phase for high- voltage tension (HT) consumers depending on utility requirements 8 Current reversal Checks whether the value of the Identification of:  Activephase All categories active current is negative or not  Meter failure or meter current connection failure  Tamper conditions 9 CT overload Checks whether the current value Identification of:  Phase current All categories is above the defined threshold  Meter damage through value overloading  Sanctioned load violation 10 Current missing Checks whether the phase Identification of:  Phase current All categories current is zero or less than the  Meter failure: under- specified threshold value recording  Tamper conditions 11 Power Factor Checks whether the PF data  Proper asset/network  Power factor All categories (PF) are within the threshold range management defined for Low and High PF  Checks for healthiness of meter and meter data recording accuracy 12 Manual reset Checks whether the meter reading Identification of:  Meter events All categories reset duration is other than the  Tamper condition data historical value or predefined  Meter failure value (a flag event is raised whenever the meter is reset in between the billing period) 13 Real-Time Clock Checks that the meter’s clock Identification of:  Meter events All categories (RTC) failure accuracy is in line with the HES  Meter failure data clock time. 14 Internal ratio Cross-verifies the CT ratio value Identification of:  CT Ratio All categories at site with the consumer master  Tamper condition data value  Non-updating of data from site 15 Magnet tamper Checks for meter tamper-event Identification of:  Magnet Flag/ All categories flags (the meter records a flag  Tamper condition Magnet event whenever it senses the Tamper presence of a magnetic field; the meter starts recording high Imax values than rated value for longer duration and this value is checked) 16 Load unbalance Checks whether the phase Identification of:  Phase current All categories current values in three phases  Tamper conditions (stringent for are unequal and whether the  Meter failure: under- HT consumers value of each exceeds the stated recording based tolerance range  Load unbalancing by on utility consumer requirement) 12  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Type of Electrical Consumer No. Exception Function Benefits/Use Cases Parameters Categories Check Considered 17 Cover open Checks for any cover-open event Identification of:  CoverOpen All categories flags (the meter records each time  Tamper condition Flag/Cover its cover is opened and flags the Open Tamper event for notification) 18 Potential missing Checks cases where the current Identification of:  Phase All categories with load value is recorded but with voltage  Tamper condition voltage running value as zero  Meter failure: under-  Phase current recording 19 Missing data/data Identifies instances of missing Identification of:  Meter billing All categories download gap data on periodic or on-demand  AMI component failure data/full data polling  Tamper conditions 20 ToD (time of day) Checks whether the meter Identification of:  Kilowatt- Consumer consumption has recorded the ToD slab  Revenue leakage hours (kWh) category check consumption for the particular in different having TOD consumer category ToD slabs rates 21 Meter pulse Checks that the number of pulses Identification of:  Physical All categories overflow check does not exceed the maximum  Meter failure verification to threshold (the meter sets a Pulse check Pulse Overflow flag when the energy Blinking consumption in a given interval exceeds the range of the interval) 22 Test mode check Checks whether data was  Meter data validation  Meter billing All categories collected while the meter was in data/full data test mode 23 Energy sum Checks the total cumulative con- Identification of:  kWh All categories check sumption of incoming data against  Tamper conditions the total consumption of various  Meter failure slots over the same time period  Energy Auditing 24 Interval spike Checks incoming data to identify Identification of:  kWh All categories check intervals with evidently high usage  Tamper condition relative to surrounding intervals  System fault 25 Unit of Checks to ensure that the Unit  Meter data validation meter data All categories  All Measurement of Measurement (UoM) of the parameters (UoM) check incoming data matches the UoM specified in the meter’s profile. 26 Usage of Checks and validates data if Identification of:  kWh/kVAh All categories inactive meters consumption is shown for inactive  Meter failure meter  Tamper conditions 27 Negative Checks whether the meter Identification of:  kWh/kVAh All categories consumption reading is lower than the last  Meter failure checks meter reading 28 kVARh check Identifies intervals where the Identification of:  Activeload Categories reactive load (kVARh) is present  Tamper conditions (in kWh) and having kVARh- and the active load (kWh) is not.  Meter failure reactive load based billing (in kVARh) Sample illustrative screen shots of these checks are provided in Appendix D. A sample output of an exception-generation check related to “Current Missing Check” appears in Figure 5. 1. Data Analytics in Power Utilities  13 Figure 5: Sample Exception Generation Check Output Current-Missing Check The data analytics system checks whether the phase current is zero (or less than the specified threshold value) in the meter data received under each time stamp. The following screenshot shows sample output for the system-generated exception case: This particular check is performed to identify the following:  Meter failure: Under-recording  Tamper conditions  Proper asset/network management  Overall health of the meter and accuracy of meter data recording The aforementioned checks are the basic data-exception checks that can be performed on meter data in non-AMI and AMI scenarios for all consumer categories. However, the actual number and frequency of checks will depend on such factors as the number of parameters received, the requirements of the various consumer categories, and the utility’s business requirements and focus areas. An illustrative example of frequency of these exception checks for various categories of consumer is provided in Table 6. In the AMI scenario, the volume of data varies with the frequency of meter polling and number of meter parameters recorded. As a result of the high resolution of the data received from meters, the number of exception checks could be much higher in the AMI scenario than in the non-AMI scenario. Table 6: Illustrative frequency of Meter Data Exception Check Consumer Sanctioned Billing Frequency of Reading Type Full Data Reading Segment Load Type Exception check A >299 kW AMI/Non-AMI Monthly Monthly Monthly B 99–299 kW AMI/Non-AMI Monthly Monthly Monthly C 10–99 kW AMI/Non-AMI Monthly Quarterly Quarterly D 1–10 kW SMRD Monthly Quarterly/As required Quarterly/as required based on exceptions based on exceptions E 1 kW SMRD Monthly Quarterly/As required Quarterly/as required based on exceptions based on exceptions 14  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities 1.4.4.1 Setting Threshold Values Analysis of meter data is about estimating the quality of data using rule-based assessments, which trigger exceptions (or alerts) whenever the monitored metric goes above or below a user-defined threshold. The utility defines exceptions by specifying the threshold values and data to be included in the check. The thresholds are set in the analysis software system based on factors such as the utility’s previous experiences, regulatory guidance, business requirements, power network operational requirements, and asset performance requirements. The threshold values are updated from time to time based on the maturity the utility achieves in each analysis it performs and on changes in any of the business scenarios. While the meter has inbuilt algorithms to register events according to the thresholds defined, the utility has to further develop a list of exceptions to monitor or set an additional filtering layer to extract meaningful outputs based on its requirements. The detailed threshold values for key exceptions as of IS 16444 standards is given in the Appendix H. 1.4.5 MIS Reports Management Information System (MIS) reports generate a detailed information summary than can be used for decision making, coordination, control and analysis. Table 7 shows a list of MIS reports that should be generated by a utility, along with recommended frequencies for better performance management. Table 7: MIS Reports Bill Processing Step No. MIS Report Output after which Report Frequency Report Summary is Generated 1 Planning and Numbers Prior to meter reading Once a  Separate reports for AMI and Scheduling Report each month month non-AMI scenarios  Outlines the meter reading sequence for a month 2 Base Meter Numbers and Meter reading Daily  Area-wise summary report after Reading percentage initial base meter reading (in Performance the Non-AMI Scenario) Report  Mentions the number of meters read and unread as well as any remarks (e.g. “gate of premises locked”)  Estimates the success rate (as a percentage) of meter readings attempted for performance monitoring 1. Data Analytics in Power Utilities  15 Bill Processing Step No. MIS Report Output after which Report Frequency Report Summary is Generated 3 Communication Numbers and Meter reading Once every  Shows the status of successful Success Rate percentage 15 days and unsuccessful remote meter Report readings  It is generated in the AMI Scenario and is equivalent to a base-meter-reading performance reading in the Non-AMI Scenario 4 Follow-up Meter- Numbers RQC Daily  Gives the meter serial number Reading Report details of failed RQCs requiring further on-site verification for resolution 5 Monitoring Report Numbers RQC Daily  Area-wise reports providing an overview of number of meters unread due to meter inaccessibility (premises locked, box locked, obstacles, entry not allowed, etc.), faulty meter, RQCs status, toggle cases* (ToD AMI Scenario), disconnected cases, etc. 6 Monitoring Report Numbers BQC Daily  Category-wise reports providing status of BQCs 7 Bill Distribution Numbers Invoicing Once a week  Reports generated area- Report/ wise during a billing cycle Disconnection summarizing the number of Notice Report bills/disconnection notices distributed with or without Proof of Delivery (PoD) and the number of undistributed bills 8 Reconciliation Numbers Invoicing Once a  Generates area-wise reports on Report month the number of meters read and unread as well as consumers billed and invoiced 9 Non-Billed for Numbers Invoicing Once every  Generates area- and category- Over 60/90 Days 60 or 90 wise reports on the number of Report days consumers having provisional bills** for 60/90 days 10 Current Demand Numbers Invoicing Daily  Compares and provides and Billed Units statistics on total units billed as Report well as the amount billed during the present month (compared to the same month in the previous year) and any deviations 11 Collection Detail Numbers and Bill distribution Monthly  Area- and category-wise Report percentage reports providing data on the number of consumers paid, categorized by mode of payment used and respective amount paid 16  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Bill Processing Step No. MIS Report Output after which Report Frequency Report Summary is Generated 12 Due Date MIS Numbers Bill distribution Daily  Area-wise due date report Reports issued to various head cashiers at collection centers  Helps in resource planning 13 Demand and Amount and Completion of billing Daily  Area- and category-wise Collection Report percentage cycle report providing amount billed, variations amount collected (including subsidies, if any) and collection efficiency – both month-to-date (MTD) and year-to-date (YTD) 14 Live and Numbers Not applicable Twice a  Area-and category-wise report Disconnected week on the number of consumers Arrears Report that have defaulted or disconnected, including their arrears amount and arrears ageing (or days delinquent) 15 Disconnection Numbers Not applicable Twice a  Area- and category-wise report Orders Report week providing the total number of disconnection orders generated based on arrears ageing (in days) and arrear amount  Helps in prioritizing disconnections 16 Billing Status Percentage Invoicing Monthly  Area- and category-wise report Report providing percentage of total billing done for various billing statuses (average/provisional/ actual) 17 Amount Billed Numbers Invoicing Monthly  Area-and category-wise report per Consumer providing total amount billed for Various Bill per consumer for various billing Statuses Report statuses (e.g., average billing, provisional billing) 18 Sundry Adjustment Numbers Invoicing Monthly/  Category-wise report providing Analysis Report Quarterly the number of consumers with sundry adjustments in terms of billing amount and units of energy consumed 19 Billing Behavior Percentage Invoicing Monthly/  Area- and category-wise report Report Quarterly providing the number of times consumers have been billed using different billing methods (i.e., provisional billing, average billing compared to the total billed generated in a year) 20 Payment Behavior Percentage Completion of billing Monthly/  Area- and category-wise report Report cycle Quarterly providing the percentage of consumers paying more than four, eight, etc. times (or non- paying) in a year 1. Data Analytics in Power Utilities  17 Bill Processing Step No. MIS Report Output after which Report Frequency Report Summary is Generated 21 Billing and Payment Percentage Completion of billing Monthly  Area- and category-wise report Status Report cycle comparing total billing with payment received in each billing cycle 22 Pending Arrears Numbers Completion of billing Monthly/  Area- and category-wise report Trend Report cycle Quarterly providing total pending arrears at the end of the billing cycle 23 Suspected Numbers Not applicable Monthly/  Area- and category-wise Defaulter Quarterly/ report providing the number of Consumers Report Yearly suspected default consumers (i.e., consumers with no payment and no meter reading during the applicable time period) 24 Meter Status Numbers and Meter reading Monthly  Area- and category-wise report Report percentage providing status of meter health during the latest billing cycle (e.g., okay, burned out, defective) 25 Meter Ageing Numbers and Not applicable Monthly/  Area- and category-wise report Status Report percentage Quarterly/ providing meter ageing status in Yearly terms of number of years 26 Meter Data Failure or Meter reading Monthly  Consumer-wise summary report Validation Checks success providing status of exception Consolidated status checks performed on meter Report data * Toggle cases refers to selecting between ToD and non-ToD tariffs, as ToD is applicable for only a few hours. ** Provisional billing is a commonly used term among Indian distribution utilities. It refers to bills that are generated in case the meter is faulty or cannot be read for any other reason. Sample illustrative screenshots of MIS reports appear in Appendix E. 1.5 Advanced Revenue Cycle Management Analytics Once the core MBC analytics has matured in a utility and the desired objective of having correct meter reading and billing is achieved, utility may leverage upon advanced revenue cycle management analytics to further its focus on revenue protection, improved energy efficiency, consumer engagement, enhanced system integrity, improved asset management etc. 1.5.1 Data Analytics A smart meter has provisions to provide only billing data and full meter data separately depending on utility requirements. The full data includes billing reads, events data, instantaneous data etc. which can be used for enabling various use cases. A list of various use cases based on smart meter full data is provided in Table 8. 18  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Table 8: List of Use Cases Based on Smart Meter Data Analytics Functionality Analytics Software Type of No. Use Cases Data Accomplished/ Approach Environment Analytics Expected 1 Energy  Weatherforecasting  Performsshort-,  Short-term  Short-,  Predictive forecasting  Historical data of medium- and long- forecasting medium- energy procured term forecasting. for day-ahead and long- purchase of power term power  Long-term procurement forecasting for management Power Purchase software Agreements (PPAs) and network planning  Mid-term forecasting for billing efficiency improvement 2 Predictive fault  Historical outage  Predictive Failure  PFA extends  Rlanguage  Predictive analysis and details on similar Analysis (PFA) power supply platform  Prescriptive network-asset day basis helps to predict stability and management  Electricity potential network quality by going consumption details failure probability beyond failure and network loading  Compares the detection to data from distribution actual network predict problems transformer (DT)- and asset-loading before they occur level-and consumer- trend with the  Life expectancy level energy-audit expected loading; of assets can be modules and feeder- if output deviations improved with monitoring systems (from system- proper loading  Weather forecast defined conditions) and maintenance data are detected,  Day of week details exceptions/flags are generated for analysis 3 Energy  High and low  Analyzes the  Helps ensure  R language  Prescriptive optimization voltage in network network voltage power quality in platform and volt VAR  kVARh reads fluctuation trend the network and  Statistical control and identifies prevents network analytics points where components from using specific reactive power unexpectedly software management breaking down is required by switching on capacitor banks, compensators, etc. 4 Predictive  Distribution  Performs analytics  Maintenance  Rlanguage  Predictive maintenance transformer (DT) on DT and feeder- of components platform and feeder meter loading data are predicted to readings  Sets peak value ensure stable  Power outage data for various asset supply  DT temperature and performance  Ensures proper oil-level information factors and loading of assets  Feeder composition analyzes instances to prevent failures  Maintenance history of violation  Instead of periodic  Analyzes DT maintenance, temperature and utility opts for outage trends condition-based maintenance 1. Data Analytics in Power Utilities  19 Functionality Analytics Software Type of No. Use Cases Data Accomplished/ Approach Environment Analytics Expected 5 Call center  Call center call  Uses analytics  Helps the  R language  Descriptive efficiency frequencies, time-of- to increase the utility plan and platform  Diagnostic day call trends and effectiveness of manage call  Statistical  Predictive issues reported all self-service center operations analytics channels by to accurately analyzing forecast workload customer patterns/ and schedules preferences  Helps the utility push customers to lower cost channels 6 Network  DT or feeder  SCADA  Reduction  SAP Business  Descriptive optimization/ usage data from  AMR of capital Warehouse  Diagnostic load balancing different seasons  GIS expenditure (BW) powered are analyzed to (CAPEX) by SAP HANA understand the by network  SAP Business loading pattern component Objects swapping Business  Load balancing Intelligence  Balanced network (BI) suite leading to fewer maintenance outages 7 Interconnected  Analyzing the  SCADA  Precise load-  SAP BW on  Descriptive feeder analysis load curve on all  AMR growth estimation HANA  Predictive interconnected  GIS  Reduced CAPEX  SAP Business feeders on same investment for Objects BI time stamp for capacity building suite better load-growth estimation 8 Energization  Single dashboard  Enterprise  Single point for  SAP BW on  Descriptive scheme showing status Resource Planning scheme progress HANA monitoring of energization (ERP) system monitoring  SAP Business scheme at various  Clear Objects BI stakeholders responsibility suite (project monitoring) center identification Sample illustrative screenshots of the above-mentioned use cases are provided in Appendix F. 1.5.2 Billing and Collection Data Analytics Running data analytics on a consumer billing database can provide insights into such areas as consumption patterns, historical payment trends, and consumer behavior. Utilities can use these insights to plan various actions in the field that will improve their operational and commercial performance. Table 9 presents a list of various use cases based on billing and collection data. 20  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Table 9: List of Use Cases based on Billing and Collection Data Analytics Functionality Software Type of No. Use Case Data Analyzed Analytics Approach Accomplished/ Environment Analytics Expected 1 Footfall  The number of studies on  System  Optimized the  Statistical  Predictive optimization consumers at number of consumers footfall leading analytics  Prescriptive at payment various centers onturning up to various to better performed centers counters (to which area various days of the consumer  structured month that particular consumer satisfaction query  Consumer due belongs to and where and increased language dates he is making payments) resource (SQL)  Gives an insight on efficiency. application workload of cashier at  Due date various counters and optimization statistics on average  Improved footfall in various Crowd counters at same time Management of day/week/month. 2 Predicting  Consumers’  Performs analytics on  Ledto a  R language  Descriptive defaulting previous payment assigning a probability reduction in platform  Predictive consumers defaults to number of consumers arrears and  SQL  Consumer credit expected to make improved application ratings defaults in next billing consumers’  Consumer cycle. payment payment behavior behavior 3 Consumer  Consumer default  Segmentation of  Took action to  Rlanguage  Descriptive segmentation history consumers by number improve upon platform  Prescriptive on default  Arrear amount of times they have the reduction of payments  Bill amount previously defaulted on defaults payments  Prioritization of cases for action 4 Migration to  Consumer mode  Identification of target  Cost  Statistical  Diagnostic digital/online of payment consumers for migrating optimization analytics  Prescriptive payment  Payment behavior to online payment  Cycle time performed of consumers modes reduction 5 Consumer  Payment behavior  Performs analytics on  Improved  Rlanguage  Descriptive segmentation  Number of times various consumers consumer platform  Prescriptive on payment paid and their payment payment behavior  Bill amount behaviors. behavior and  Segmented based on retention of their payment behavior. good consumers 6 Theft  Previous  Performs analytics and  Reduction in  Rlanguage  Descriptive analytics enforcement identifying areas where AT&C losses platform  Diagnostic records. power theft is high and  Predictive  Event reports and assigning probability on  Prescriptive notification history. theft occurrence.  Load survey data.  DT Energy audit data. 7 Meter  Connection  Complaints registered  Fewer defective  Statistical  Descriptive reading number, meter for meter reading bills analytics  Diagnostic and billing number, previous and billing generated performed  Prescriptive complaints consumption monthly analysis pattern, complaint history, consumer category 1. Data Analytics in Power Utilities  21 Functionality Software Type of No. Use Case Data Analyzed Analytics Approach Accomplished/ Environment Analytics Expected 8 Provisional  Number of  Analysis of provisional  Reduction in  Statistical  Descriptive billing provisional bills billing (average billing) provisional analytics  Diagnostic analysis (by year and owing to inaccessibility billing performed similar month), of meters - monthly repeat complaints (by segment), category of provisional billing (e.g. premises locked or entry not allowed) 9 Faulty-meter  Number of faulty of faulty-  Analysis  Reduction in  Statistical  Descriptive analysis meters and ageing meter complaints and provisional analytics  Diagnostic (i.e., duration) of actual meters replaced billing performed faulty period – by monthly  Fewer meter year, segment, replacements sanctioned load, meter manufacturer, and meter type Sample illustrative screenshots of these use cases are provided in Appendix G. 1.6 Key Issues/Risks Associated with Data Analytics Smart meters and other IoT-enabled systems allow utilities to use analytics to gain insights into their operations – insights that can be put to use in improving operational efficiency. However, incomplete metering or unavailability of correct data can be a bottleneck for running various analytics algorithms to derive the full potential of analytics systems and ensure a return on the investment made. Table 10 depicts some of the issues associated with data analytics, along with possible solutions for each. Table 10: Key issues and solutions associated with data analytics No. Issues Impact Solutions 1 Implementation of an  Investment made will not  Proper blueprinting of analytics logics analytics system without yield the envisaged benefits (algorithms) before implementation proper orientation of utility  Representatives from all functional requirements departments of the utility serving on core implementation team 2 Defining inappropriate  Outputsgenerated will  Definition of threshold values in line with threshold values, e.g.: not be useful for proper utility requirements  Same threshold values for operational decisions  Selection of appropriate threshold values all category of consumers/ based on utility’s maturity level and geographical areas requirements  Not updating threshold values in line with the changing business conditions 22  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities No. Issues Impact Solutions 3 Unavailability of required skill  Higher dependency on of “champion” teams with the  Identification set within the organization external system integrators organization to take up initiatives and run and Original Equipment the project Manufacturers (OEMs) 4 Inaccuracy and inconsistency  Wrong data will lead to  Establishmentof a system to ensure the in data received wrong interpretations and accuracy and quality of data less precise outputs 5 Inefficient field-level actions  Desired objective of  Establishment of a monitoring mechanism to based on analytics results analytics will not be achieved ensure there is improvement in operations over time 6 Communication failures where  Theft prediction efficiency  Facilitatemanual data downloading and successful polling of meters will not be optimal hardware trouble shooting in the field cannot be done  Load growth estimation will  Stringent service-level agreement (SLA) be inaccurate conditions for timely rectification of cases 7 Mismatch of DT meter and  Ithampers proper energy  Reading of DT meters and consumer meters consumer meter reading dates auditing for loss-level at the same date and time, if possible calculation 8 Consumer meter is faulty and  Theft prediction efficiency  Estimation of consumption based on meter cannot be read will not be optimal previous consumption records  Estimation of consumption by analyzing the feeder- or DT-level metering data along with other consumer meter points 9 Ad hoc architectures and poor  With ad hoc architectures  Core-level standardization has to be brought integration and without proper IT in to successfully integrate systems infrastructure in place, inter-  Utilities should redesign their enterprise and intra-system integration architecture to allow flow of data for will be unable to fully leverage operational and meter-to-cash applications the benefits of the data 10 Change management  Traditional organization  Effective use of data analytics requires structures lead to very removing traditional silos and combining limited applicability historically disparate groups of modern systems implemented 11 Incomplete AMR/AMI metering  Difficultyin fetching data  Implement manual reading of non-AMR remotely from non-AMR meters using Hand-Held Units (HHUs) meters or other conventional methods at fixed  Unavailability of full data for intervals for collecting full data carrying out energy auditing, load forecasting, outage management, etc.  Data analytics use cases are limited to billing 1.7 Benefits of AMI over AMR for Improved Data Analytics In AMI systems, the real-time meter data can be pushed or pulled to the head-end system (HES) automatically in a configurable frequency (i.e., at fixed intervals) as well as on demand. AMI systems integration pushes a huge volume of data to the utility’s servers. The number of parameters that can be captured by a smart meter (per utility requirements) will be higher than is possible using non-AMI 1. Data Analytics in Power Utilities  23 meters. This higher “resolution” and increased volume of data make it possible to run more potential use cases with advanced analytics systems – which in turn generate insights that can be used to guide efficiency-improvement initiatives in utilities. A brief comparison of the functionality of AMR-enabled meters and AMI meters is provided in Table 11. Table 11: Comparison of Functionality of AMR and AMI Meters Functionality Normal Meter (AMR-Enabled) AMI Meters Data storage  Yes  Yes Tamper detection  Yes  Yes Communication port  Optical or RJ11 or both  Yes Meter reading  Automated, with a defined schedule  Automated, with a defined schedule (meter (meter pushing the data to HES; usually pushing the data to HES; frequency can be as once a month) low as 15 minutes)  Meter can be manually polled on need  Meter can be manually polled on need basis basis (Pull mode) (Pull mode)  Optical Port is available for download of  Optical port is available for download of meter meter reading using CMRI reading using CMRI Remote reading  Through external/retrofit modem or modular but integrated with meter  Built-in  Separate power supply for modem housing  No separate power supply for communication Remote communication  GSM, GPRS or RF Drive-By  GPRS mode  Sophisticated RF with router/DCU/gateway capable of self-healing  Plug-and-play needs no commissioning Data availability through  Mainly billing data  All data including load profile, events, outages remote reading Two-way communication  No  Yes, event notification Configuration of  Limited  Allallowed parameters even support remote parameters in meter firmware update Communication protocol  Proprietary  Open Standard (DLMS) Software support  Limited  Standard; many types of Commercial  Meter Manufacturer Dependent Off-the-Shelf (COTS) software available  Mainly standalone desktop-based  Web-based applications, can be accessed system from anywhere  No centralized access Integration  Meter Manufacturer Dependent  Standard,XML based or Service Oriented architecture Parameter recorded  Billing  Billing  Load survey  Load Survey  Events  Events  TOU  TOU  Varies by utility and manufacturer  Power outages  Transactions  Standard-based 24  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Functionality Normal Meter (AMR-Enabled) AMI Meters Data usage  Limited, though data is stored in meter data is transferred on near real-time basis  All to central system, making it available for analytics Communication  Monthly  Configurable;can be configured for as low frequency as 15 minutes (i.e., data is transmitted every 15 minutes) Load control  No  Yes Remote connection/  No  Yes disconnection Additional functionality  No  Yes net metering/pre-paid  Separate Meters  Configurable Primary applications  Monthly Billing  Utilityoperations  Demand response  Billing and customer information system  Customer data display  Outage management On-demand response None  Load control  Demand management  Critical peak rebates Customer feedback Billing  Billing  Loadpattern  Website or mobile phone app Energy accounting Monthly billing based real time or daily, depending on data  In communication frequency New capabilities None  Power quality  Load balancing  Capacitor bank switching with additional devices  Transformer load management  Automated outage management Security Varies by meter manufacturer and also  Varies by meter manufacturer and also based based on utility requirement mentioned in on utility requirement mentioned in RFP RFP (meter standards version) (meter standards version) Administration Limited to metering and billing operations  AMI provides data integrations to multiple and related services utility business systems in addition to metering and billing systems like OMS, DMS, NMS,* load forecasting, etc. * A Distribution Management System (DMS) is a collection of applications designed to monitor and control a distribution network efficiently and reliably. An Outage Management System (OMS) is an electronic system developed to identify and correct the outages in the electrical network. A network management system (NMS) is a system designed for monitoring, maintaining, and optimizing a network. Key features of smart meters include faster network restoration with outage notification from the meter, remote meter reading, remote disconnection/reconnection, and faster tamper detection. The real-time data allows utilities to improve in such areas as volt/VAR control, asset management, and network planning. 1. Data Analytics in Power Utilities  25 AMI technology facilitates two-way communication between utilities and consumers, which is not possible using AMR technology. This allows both utilities and consumers to initiate fast actions as required. Providing real-time information to consumers helps them participate in efficient and effective energy conservation efforts, while providing real-time data to utilities gives them an “as-operated” view of the network. Running advanced data analytics on real-time meter data from AMI systems can benefit energy utilities in the following ways:  Improved customer targeting and segmentation: There are compounding benefits for utilities to deploy consumer analytics in their operations. Detailed knowledge of consumers can increase the lifetime consumer value of residential and commercial users. In an increasingly competitive market, understanding consumer behavior and the needs of customer groups is key to successful retention of customers. Through targeting specific customers, companies are able to discern customer use patterns, which allows them to develop and deliver their message more effectively to customers.  Improved energy leakage analytics and revenue protection: Smart meters already offer better tampering resistance, but analytics can help advance the detection of power thefts, thus preventing revenue losses. Analytics checks monthly consumption and energy bills and identifies any discrepancies that might indicate fraud.  Effectively target energy efficiency programs and demand response analytics: A deeper understanding of how consumers use energy empowers energy utilities to develop targeted energy efficiency programs.  Improved billing and payment options: When more information is known about users, companies are able to customize billing to user segments based on rate class, program participation, interests, efforts to conserve energy, and so on.  Reduced network maintenance and better assets management: Metering at Distribution Transformer (DT) and feeder points provides insights into network and asset-loading conditions. This enables the utility to improve network and asset utilization while reducing the chance of network failure. In India, AMR technology has been partially rolled out in several utilities, covering a large proportion of their high-value industrial and commercial consumers. Meanwhile, over the last few years, Ministry of Power (MoP) schemes such as IPDS and UDAY have helped accelerate the adoption of smart meters. More recently, several utilities have proposed advancing to AMI technology. One of the key roadblocks preventing the adoption of smart metering technology has been the high cost of smart meters. With the recent EESL aggregated procurement of smart meters, however, the cost per meter has fallen by more than 50 percent. India’s AMR metering standards support Time-of Use (ToU) pricing.8 However, various IoT-enabled services, DSM initiatives, and other initiatives requiring real - time data will require AMI implementation. 8 With time-of-use (TOU) pricing, electricity tariffs rise during peak demand periods and fall during low-demand periods. 26  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities While a full-scale adoption of AMI would be ideal, utilities may follow a phased approach and gradually enhance their AMI penetration. The following key technical requirements may be useful in a phased transition to AMI:  Standardization of communication technologies and data-exchange protocols. In other words, data-exchange protocols that support AMR should be able to support two-way communication in AMI systems.  Existing AMR head-end software solutions should be scalable so they can adopt AMI in future.  Utilities should deploy smart meters initially, then make relevant technical upgrades where possible to enable two-way communication and other operational features (such as DSM initiatives, ToU tariffs, and remote disconnect/reconnect) that will allow them to adopt AMI in future.  Network Interface Cards (NICs) should be easily replaceable in the field so that without replacing meters, communication infrastructure can be upgraded to support future technologies, such as the migration from the third to the fourth generation of mobile broadband Internet (3G to 4G). 1. Data Analytics in Power Utilities  27  rocurement and Deployment 2. P of Analytics Systems 2.1 Data Analytics System Architecture Data analytics systems generally follow a uniform architecture featuring the following key components:  Data sources  Data integration  Analytics hub  Data sciences/analytics applications  Data delivery points/visualization interface  Work/case management However, depending on the industry and overall system architecture, the data sources, integration requirements, and dashboard components may change. Figure 6 shows an illustrative architecture of a data analytics system for a power distribution utility. The details of the various components of an analytics system are summarized in Table 12. Figure 6: Data Analytics System Conceptual Architecture WORK/CASE Feedback DATA SCIENCES/ MANAGEMENT ANALYTICS Leads APPLICATIONS DATA SOURCES DATA INTEGRATION Data Business Intelligence DATA VISUALIZATION Data Data Data ANALYTICS HUB Predictive Analytics Ingestion Quality Maps/Charts System of records Data In-Memory Data Master Data Aggregates Reports and Transformation Management Dashboards Statistical/ Data Lake Database ML Models Streaming ENTERPRISE APPLICATIONS Asset Operational Work Order Accounting Management Customer CRM Systems MDMS ERP Systems Management Service Systems Systems Systems Systems 28  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Table 12: Details of Various Components of Analytics System Components Details Data Sources A utility’s data sources are its operational and back-office systems. In data management terms, the overall information storage system is the “system of record” – and is sometimes referred to as “the source of truth,” meaning a trusted data source. Due to the nature of these purpose-built systems, they tend to be siloed – that is the data tend to be stored in different ways without a common organizational principle. The three main data sources are as follows: Information Technology Operational Technology (OT) Third Party Systems (IT) Systems Systems  Examples  Billingengine/  SCADA (supervisory control  Weather forecast of Input Enterprise Resource and data acquisition) system systems Sources Planning (ERP)  OMS (outage management  Social media software system)  Websites  Customer master data  GIS (geographic information  Call center system)  In-house IT  AMI/MDMS (advanced applications metering infrastructure/meter data management system)  ADMS (advanced distribution management system) Volume Medium High Low Data Type Structured Structured/semi-structured Unstructured Analytics hub An analytics hub is different from a data warehouse or a simple “big data” repository. It encompasses a more modern approach in which “in-memory”* technologies are used in conjunction with “big data” hubs to address Business Intelligence (BI) and real-time analytical computing needs. This architecture scales vertically and horizontally and uses computing resources efficiently – in a cloud or a traditional data warehouse. In a real-time or a near-real-time analytics scenario, analytics are deployed on the in- memory system or database to compute new results even as new data sets become available. Models are typically developed in languages such as Python, Scala and R to execute statistical and machine- learning algorithms. Data Data integration poses several challenges that can be understood in terms of the “four V’s” of big integration data: variety, volume, velocity and veracity. The variety of data includes structured, unstructured, time series, events/alarms, and streaming – along with data sources external to the utility, such as weather data. The volume of data can be significant if a historical time-series data are used as data use grows exponentially. Since real-time execution of certain analytics is important, building capabilities to eliminate latency in data integration (i.e., improving velocity) is important. Lastly, knowing which data are the correct data (i.e., its veracity) is usually a big area of concern and usually requires careful analysis. Examples of software used for data integration include Informatica, Talend, Oracle Goldengate, and SAP SLT and Data Services. Data sciences/ Analytics-model development differs from software development in terms of its approach. It is common analytics to have an analytics (or data science) team performing the Exploratory Data Analysis (EDA), devising applications models, and testing models or model “ensembles” (i.e., groups of models). These models are often tested over time to minimize false positives and negatives before they are moved into production. In addition, several vendors provide pre-built analytics models as a part of their software applications; these models have been tested at various utilities and need minimal modifications to be incorporated into production. The models – which may be descriptive, predictive or prescriptive – are built using Natural Language Processing (NLP), statistical (open-loop/closed-loop) machine-learning models, and artificial intelligence models (sometimes in an ensemble), among a variety of other approaches. Examples of software used include R, SPSS, SAS, Python, Scala, SAP Predictive Analysis Library (PAL), and Oracle R/OAC/Data Miner/OBIEE. 2. Procurement and Deployment of Analytics Systems  29 Components Details Data Data visualization tools currently on the market can perform extensive modelling themselves, but visualization are used mainly for data discovery and presentation of analytics results and can display insights in very crisp and clear manner. Examples of software used include the SAP Business BusinessObjects Intelligence (BI/BO) suite, Tableau, Qlikview, and Oracle Data Visualization. Work/case It is important that the analytics are synthesized to drive actions. For example, if the analytics provide a management prescriptive action to analyze electricity theft at a premise, there has to be a mechanism to execute the action. This can be done with existing work management systems by extending their functionality, but it is more common to manage such scenarios with a Case Management** solution. * In computer science, in-memory processing is an emerging technology for processing data stored in database. Older systems are based on disk storage and “relational” databases maintained using a Relational Database Management System (RDBMS). Because stored data is accessed much more quickly when placed in Random-Access Memory (RAM) or flash memory, in-memory processing allows data to be analyzed in real time, enabling faster reporting and decision-making in business. ** Case management refers to the process of resolving an incident or issue relating to the utility’s business, staff or customer, and being accountable for the resolution of the incident or issue. Table 13 showcases several market-available technology solutions currently in use in various utilities for analytics systems. Table 13: Technology Solutions in Use in Various Utilities for Analytics Systems Components TPDDL* ComEd** PECO*** Data sources  ITsystems: Enterprise Resource  ITsystems: Oracle MDM,  IT systems: Oracle MDM, Planning (ERP) systems; SAP IS-U; Accenture Customer/1 CIS, Accenture Customer/1 CIS Metering, Billing and Collection ABB Asset Suite 8  OT systems: Intergraph (MBC) systems  OT systems: ABB DMS/OMS, In-service, Esri GIS,  Operational Technology (OT) Esri GIS, Operations Optimizer Oracle DataRaker systems: SCADA, OMS, GIS, etc.  Third-party systems: weather  Third-party systems:  Third-party systems: weather data, social media, web weather data, social data, social media, websites, etc. services, etc. media, web services, etc. Analytics hub  Data warehouse systems:  Data warehouse systems:  Data warehouse systems: RDBMS, SAP BW, SAP HANA. RDBMS, custom Corporate RDBMS  Big-data lakes**** using Hadoop Data Warehouse  Big-data lakes using  Big-data lakes using Exadata, Exadata, Cloudera Cloudera Data integration  Informatica  Informatica  Informatica  Oracle Goldengate  Oracle Goldengate  Spark  Spark  Kafka  Kafka Data sciences/ based on the R  Platforms  R  R analytics programming language  Python  Python applications  Oracle R  Oracle R Data  SAP BusinessObjects suite  Qlikview  Qlikview visualization  Tableau  Oracle Business Intelligence (BI)  Tableau  Oracle BI Work/case  SAP ECC  Work Information Management  Meterwork management management System (WIMS) system * Tata Power Delhi Distribution Limited. ** Commonwealth Edison, commonly known as ComEd, is the largest electric utility in the U.S. state of Illinois. *** PECO (formerly the Philadelphia Electric Company) is an electric and natural gas utility. It is a subsidiary of Exelon Corporation, the United States’ largest regulated utility in terms of customer count and total revenue. **** A data lake is a large reservoir of enterprise data stored in a cluster of commodity servers that run software such as the open-source Hadoop platform for distributed big-data analytics. 30  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Approaches for Procurement and Implementation of 2.2  Analytics Systems 2.2.1 Road Map Before a utility adopts a data analytics system, it must have a clear organizational vision and mission. The TDWI Analytics Maturity Model9 showcases the journey a utility must take to maximize the end-to- end business benefits that big-data and analytics solutions can potentially offer. The journey can be divided into five stages, details of which are shown in Table 14. While most South Asian utilities have already implemented software solutions for billing and customer-related processes, they have not yet implemented the OT systems. As a result, the utilities will have to undergo the transformation journey depicted in Figure 7 to fully leverage the benefits of data analytics. Table 14: Utility Transition Stages in Adopting Analytics Systems Stage Description The utility has a low awareness of analytics or its value across the value chain. It has just begin Nascent stage integrating OT systems into its IT framework, and has standalone reporting capabilities within each business area. The utility has conducted an initial assessment of data analytics and has some form of strategic plan in place. It may have standalone dashboards and reporting capabilities and disparate Pre-adoption stage analytics processes within each business area; however, difficult obstacles remain, such as data integration skill-set shortcomings with regard to tools and human resources. The utility has strategic and scalable technical capabilities to deliver enterprise-wide analytics Adoption stage solutions across business areas. There will be a significant shift toward data-driven business processes and a redefinition of the operating model. The utility has achieved a complete digital transformation, and all business processes and Maturity stage operations are now optimized using advanced analytics. The utility has generated significant business value from analytics by continuously focusing on Visionary stage sharpening its technological capabilities. Source: TDWI Analytics Maturity Model. Figure 7: Transition Stages for South Asian Utilities 1 2 3 Implementation and Adopting Core Adopting Advanced Integration of Data analytics related to Data Analytics Sources MBC Systems • Implementation of • Adoption of RQC, • Leveraging high IT and OT systems BQC, Exception resolution “Big • Integration of OT checks Data” for advanced with IT systems • MIS Reporting analytics systems (data Science) 9 See https://www.microstrategy.com/getmedia/9b914607-084f-4869-ae64-e0b3f9e003de/TDWI_Analytics-Maturity-Guide_2014-2015. pdf, p. 9. 2. Procurement and Deployment of Analytics Systems  31 2.2.2 Procurement Models Procuring the hardware and software necessary to deploy and implement an analytics system can be done using one of three models: consolidated, component-wise, or “software as a service” (SaaS). 2.2.2.1 Model 1: Consolidated Procurement Model In the consolidated procurement model, the utility hires a System Integrator (SI) to render end-to-end services to implement the analytics system – including an “as-is” study,10 system design, procurement of system components, implementation and integration. The utility’s role includes organizing the call for bids/tenders, monitoring the implementation and making payments to the system integrator. Utilities with no prior experience in IT implementations typically adopt this “consolidated” model, which is illustrated in Figure 8. In India, for example, Kolkata-based utility CESC has adopted this model for deployment of analytics solutions. EESL’s “cost-plus” model is very similar to this model. As a third-party nodal agency, EESL is operating a BOOT (build, own, operate and transfer) model to help utilities take up efficiency improvement initiatives. 2.2.2.2 Model 2: Component-wise Procurement Model In the component-wise procurement model, the utility first procures the necessary hardware, then hires a system integrator for software implementation and system integration. This model, illustrated in Figure 9, is adopted mainly by utilities planning to expand their existing systems. Figure 8: Consolidated Procurement Model As–is study, analytics software – hardware procurement & implementation and system integration Services Improved Services Agreements and Monthly Payments DISCOM CONSUMERS System Integrator DISCOM Role: • Releases tender to procure system integrator for “as –is” study • Implements end-to-end system including hardware, software and system integration • Makes investments & monitors project deployment 10 An “as-is” study defines the current state of business processes in an organization. It is often an interim step to improving the current state by creating a new “to-be” or future-state process. 32  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Figure 9: Component-wise Procurement Model Analytics System Hardware Vendors Analytics system hardware Payments components procured DISCOM Role: Improved • Procures analytics system hardware components services independently • Release tender to procure system integrator for system implementation and integration DISCOM • Monitors project deployment CONSUMERS Agreements & Monthly Payments Analytics software implementation & system integration services System Integrator The key issue that might arise in this model will be related to interoperability of various system components that are procured separately. Any utility considering adopting this model should have sufficient technical expertise and an experienced IT team within the organization. In India, Delhi-based TPDDL has adopted this model to deploy its analytics solutions. 2.2.2.3 Model 3: Software-as-a-Service Procurement Model In the software as a service (SaaS) procurement model, the utility hires a service provider to host the systems, either on a cloud platform or physically at their host locations, and to provide other services as needed. As illustrated in Figure 10, the utility’s role is limited to managing the service provider agreements and monitoring the performance of the systems and services rendered. In the United States, PECO and ComEd have adopted this model for deploying energy-theft and meter- operations analytics services. PECO uses Oracle DataRaker, while ComEd uses Operations Optimizer, which is made by Silver Spring Networks (recently acquired by Itron). 2. Procurement and Deployment of Analytics Systems  33 Figure 10: Software-as-a-Service Procurement Model Scalable analytics software and Increased customer hardware infrastructure and data satisfaction and integration services loyalty Agreements & Monthly DISCOM Payments Software as a CONSUMERS Service Provider DISCOM Role: • Releases tender to procure software-as-a-service analytics platform to implement analytics • Service provider ensures scalability and service level agreements are met • DISCOM manages provider according to agreement and monitors performance 2.2.2.4 Model Comparison The pros and cons of each procurement model are discussed in Table 15. Table 15: Comparison of Procurement Models Procurement Pros Cons Model Consolidated and mature implementation model  Traditional  Control on the outcome can only be asserted  High probability of success as utility draws contractually on experience from previous successful  Maintenance of system requires training of internal programs staff  Resources: capability and capacity well  Inability to deploy specific resources DISCOM defined and understood knows and trusts Component-  More control over implementation and  Lack of large program experience wise execution  Depends on capacity of internal resources to scale  Flexible and ability to adapt  DISCOM responsible for performance, reliability  Change management and organizational and scalability knowledge  DISCOM accountable for delays and overruns Software as a  Short procurement and implementation cycle  Data privacy and security concerns Service (SaaS)  Vendor responsible for performance,  Control on the outcome can only be asserted reliability and scalability contractually  Lower total cost of ownership  Strong communications network and data interfaces needed 2.3 Technical Specification for Analytics Systems The technical specification of analytics systems varies with the number of smart meters in the utility area and the volume/variety of data captured from the meters, as explained in Table 16. 34  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Table 16: Hardware and Software Specification of Analytics System Hardware Specifications  The solution should be sufficiently scalable to accommodate future business growth.  The analytical system ideally should have a built-in redundancy feature with no single point of failure.  To protect the utility’s investment, the analytical system should be able to support the coexistence of at least two generations of hardware in order to avoid hardware re-deployment for future upgrades or expansions.  The solution should allow for data replication between two data centers in asynchronous mode.  The power, cooling and space parameters (Rack Unit and HDD numbers)in the hardware system must be designed to accommodate future expansion.  The operating system should be bundled with the solution including support.  The solution should be bundled with network switch.  The platform should be capable of the standard archiving process  The platform should be persistent such as for long-term regulatory and analytics data, records that will be used in time-based analysis; need a clearly defined policy to include on-going integrity checks and other long-term reliability features, and to address the need for data-in-place upgrades. Software Specifications (Analytics and Advanced Analytics) Type Features Analytics Data Integration and Processing Solution  The Extract, Transform and Load (ETL) tool should be integrated with the most commonly used source systems, such as ERP systems, CRM systems, and RDBMSs.  The ETL tool should allow for the aggregation of all source databases. The data will load into the data store, and loads can be scheduled.  The solution should allow for real-time data ingestion (i.e., the process of obtaining and importing data for immediate use or storage in a database) and real- or near-real-time reporting capability from IoT devices, smart meters and other sources of data.  The solution should have integration tools for transforming data within the analytics system. Data Management and Administration  The solution should allow the user to access and manage data through an intuitive user interface. Manageability and Security  The solution should allow full configuration of system administration, and should provide monitoring tools for all components.  The system should ideally provide a secure authentication mechanism to prevent eavesdroppers/ attackers masquerading as (i.e., pretending to be) valid users; for example, the Kerberos network authentication protocol.  The solution components must be integrated with the existing Lightweight Directory Access Protocol/ Active Directory (LDAP/AD) system. Users should only be able to log in via their corporate LDAP/AD account. However, the LDAP/AD system also can be used as a standalone.  The system should ensure that data sets are secure (so that only authorized users can view particular data sets) and that group access is read-only.  The system should be able to audit and review who is accessing what data set, to ensure that these policies are being followed and enforced. Also, log retention should be available.  The system should have the option of partial or full encryption of the data at-rest. Advanced General Features Analytics  The proposed advanced-analytics solution should be based on commonly used data science Solution languages, such as Python or R.  The solution must provide an integrated environment for running predictive and descriptive modeling, data mining, forecasting, optimization, simulation, experimental design and more.  The platform should support multi-threaded applications for data scientists. 2. Procurement and Deployment of Analytics Systems  35 Type Features Statistical Modelling Features  The solution should support basic algorithms like regression, Lasso-based regression, ridge regression, logistic regression, decision trees, random forest, support vector machines, clustering algorithms, time series modelling, topic modelling, and term frequency matrix.  The solution should be able to leverage in-memory capability to carry out statistical and forecasting calculations. Data Discovery  The proposed tool should be a single, intuitive and visual interface that allows users to find and explore data, quickly transform and enrich it, and unlock it so that anyone can discover and share analytical insights.  The solution should be able to upload/import data from spreadsheets (e.g. Excel) and text files (e.g. delimited files, such as comma-separated-values [CSV] files) into memory for analysis.  The solution should have a user-friendly, drag-and-drop interface (web-based or command line) allowing data sampling and discovery in self-service mode. 2.4 Functional Requirements of Advanced Analytics System The functional requirements of an advanced analytics system may be grouped into three areas: reporting, reliability, and storage. 2.4.1 Reporting 2.4.1.1 General Features In terms of general features, the solution should:  Provide an easy-to-use visualization capability so that users can access information – including interactive graphs, charts, and tables – collated from the multiple operational sources, thus enhancing decision making;  Allow users (those with sufficient administrative rights) to perform ad-hoc analysis on the data and distribute the results on mobile devices for on-the-go analysis;  Facilitate alerts based on user defined criteria which would be dispensed in the form of emails, portal alerts etc.;  Be able to publish configurable alert notifications by SMS or email;  Have parameterized calculations that enable dynamic filtering, ranking, calculations and display rules;  Have strong layout capabilities to provide flexibility in report layout and design;  Allow customized interaction between sections, objects, and so on;  Ensure that data are interactively prepared for analysis – including joining tables, defining custom calculated columns, and creating custom expressions; and  Be a part of an integrated solution framework, preferably from single-vendor support for better connectivity and seamless integration. 36  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities 2.4.1.2 Mobility Features In terms of mobility features, the solution should provide:  Interactive reports and dashboard viewing on tablets and smartphones for iOS and Android without requiring any redesign;  Analysis/explorations/reports to be pushed for online or offline viewing on mobile devices; and  Ideally, collaboration support for annotation on tablets. 2.4.1.3 Platform Support for All Office applications In terms of platform support for Microsoft Office applications, the solution should:  Feature seamless integration with Microsoft Office suites  Support Outlook integration  Support Excel integration, with the ability to leverage native Excel charts  Allow printing to PDF as well as exporting data in Excel and comma-separated-value (CSV) formats. 2.4.1.4 Reporting Tool Security In terms of reporting tool security, the following is required:  The solution should define screen layouts according to allowed security permissions.  User, group, object and folder security should be configurable.  Security for data restrictions should be managed from within the solution.  Security should be defined at row, column, and connection levels.  Unsuccessful login attempts should be recorded and retrievable somewhere on the system. 2.4.2 Reliability High availability (HA) refers to the analytics system’s ability to service users even when confronted with component failures. The system should ideally have redundancy at both hardware and network- component levels. Data protection capabilities let users protect data from any unauthorized access. The system should offer at-rest encryption of data to ensure data and information security. 2.4.3 Storage The analytics system should support data storage and applications on a cloud platform or on on-site physical databases. Cloud-based storage can be a cost-effective solution compared to conventional on-site physical storage solutions; it can also accelerate technology adoption in power utilities by reducing the time required to set up data centers. Table 17 compares the various cloud-based and physical database solutions. 2. Procurement and Deployment of Analytics Systems  37 Table 17: Comparison of Cloud-based and Physical Database Solutions Solution Physical Cloud Servers  Co-located  Integrated  Failure-dependent  Fault tolerant Resources  Partitioned  Unified  Performance interrelated  Performance isolated Management  Separate  Centralized full control  Manual  Automated Scalability  Plan ahead  Flexible  Over-provisioning  Scalable Renting/Costing  Per physical machine  Per amount of usage Application/Services  Fixed on designated servers  Runs on all virtual machines Data Security  High  Moderate Cost  High  Low 2.5 Sizing and Typical Bill of Quantity for Analytics System The component requirements, system design and system cost depend on the volume and variety of data and the processes the system must perform on the data. The utility should carry out a detailed hardware- and network-sizing exercise separately based on the business requirements. Table 18 illustrates a sample analytics system sizing and Bill of Quantity (BoQ) for 5,00,000 consumers in the AMI scenario; the configuration assumes that the necessary data center (including the required number of server racks) is already available. Table 18: Sizing and Typical Bill of Quantity for Analytics System Parameters Description Number of consumers  5,00,000 Number of parameters  10 Meter polling frequency  30 minutes interval Data Source Size Billing Reads 1 terabyte (TB) AMI full data 7 TB Total data size per year including OT systems (SCADA, DMS, OMS, etc.) 3.5 TB redundancy ERP and in-house IT systems 3.5 TB Total 15 TB  Scalable up to four times the current capacity  Temporary/log storage should be on an SSD/Flash drive Memory/RAM  1 TB (scalable up to 2 TB)  Approx.16 CPUs with 2.28 GHz or higher CPU* and clock speed  Thenumber and specification of each CPU shall be as certified by the software provider 38  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Parameters Description Processor type  OEM-certified processor Network interfaces  10 gigahertz (GHz), two in number Operating system  Open source (Windows, Linux, Unix) HBA** connector  Two in number, 16 gigabits per second (Gbps)  50 percent of the main setup Data redundancy  Replication support to be provided by the software vendor * CPU = central processing unit. ** HBA = host bus adapter. 2. Procurement and Deployment of Analytics Systems  39 References and Links TPDDL Smart Grid Lab overview:  http://www.tatapower-ddl.com/showcontent.aspx?this=151&f=ABOUT-US&s=Smart- Grid&t=Overview  http://www.tata.com/article/inside/tpddl-launches-state-of-the-art-smart-grid-lab  http://www.tatapower-ddl.com/Editor_UploadedDocuments/Content/TATA-POWER-DDL%20 Smart-Grid-BROCHURE-Final.pdf EESL smart-grid initiatives: http://www.eeslindia.org/content/raj/eesl/en/Programmes/Smart-Meters/ about-smart-meters.html Indian Power Sector Policies and Schemes Overview: https://powermin.nic.in/en/content/overview-4 UDAY Scheme: https://www.uday.gov.in/home.php IEEE Smart Grids: https://smartgrid.ieee.org/ IEEE Smart Grid Standards: https://smartgrid.ieee.org/resources/standards Yi Wang, Qixin Chen, Tao Hong, and Chongqing Kang. 2018. “Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges.” IEEE Transactions on Smart Grid. https://arxiv.org/ abs/1802.04117 40  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Appendices Appendix A. Description of Key Technology Terms Advanced Metering AMI is an architecture for automated, two-way communication between a smart utility meter with Infrastructure (AMI) data communication capability (via an IP address) and a utility company. The goal of AMI is to provide utility companies with real-time data about power consumption and allow customers to make informed choices about energy usage based on the price at the time of use. Automatic Meter AMR is a technology that automatically collects consumption, diagnostic, and status data from Reading (AMR) energy metering devices and transfers that data to a central database for billing, troubleshooting, and analyzing. AMR saves utility providers the expense of periodic trips to each physical location to read a meter. Big Data Big data is a term that describes the large volume of data, both structured and unstructured, that may be analyzed computationally to reveal patterns, trends, and associations – providing operational benefits to respective sectors. Big Data Analytics Big data analytics is the process of examining large and varied data sets – i.e., “big data” – to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Data Lake A data lake is a large reservoir of enterprise data stored in a cluster of commodity servers that run software such as the open-source Hadoop platform for distributed big-data analytics. DMS, OMS, NMS A Distribution Management System (DMS) is a collection of applications designed to monitor and control a distribution network efficiently and reliably. An Outage Management System (OMS) is an electronic system developed to identify and correct the outages in the electrical network. A Network Management System (NMS) is a system designed for monitoring, maintaining, and optimizing a network. Hadoop System Apache Hadoop is “big data” management platform. It is a collection of open-source software that facilitates using a network of many computers to manage and solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data. Head-End System A HES is hardware and software that receives meter data sent to the utility. HESs may perform (HES) a limited amount of data validation before either making the data available for other systems to request or pushing the data out to other systems.11 11 See https://openei.org/wiki/Definition:Head-End_System. Appendices  41 In-Memory In computer science, in-memory processing is an emerging technology for processing data stored Technologies in database. Older systems are based on disk storage and “relational” databases maintained using a Relational Database Management System (RDBMS). Because stored data is accessed much more quickly when placed in random-access memory (RAM) or flash memory, in-memory processing allows data to be analyzed in real time, enabling faster reporting and decision making. Internet of Things The IoT is an interconnection of endpoints (devices and other objects) that can be uniquely (IoT) identified using an Internet Protocol (IP) address. With the IoT, devices can be connected to the Internet to sense, gather, receive and send data, and communicate with each other and applications – via IP technologies, platforms and connectivity solutions. Net Metering Also called Net Energy Metering (NEM), net metering is a billing mechanism that credits solar energy system owners for the electricity they add to the grid.12 Non-Volatile Non-Volatile Memory (NVM) is a type of computer memory that has the capability to hold saved Memory data even if the power is turned off. Smart Grid According to the IEEE, “The Smart Grid is defined as an integration of electricity and communication, so that the electric network will be ‘always available, live, interactive, interconnected, and tightly coupled with the communications in a complex energy and information real-time network.’”13 A smart grid is not a single technology or system, but rather a central system of systems in which various types of technology are deployed. 12 See https://www.seia.org/initiatives/net-metering and https://en.wikipedia.org/wiki/Net_metering. 13 See http://docplayer.net/45366025-Co-simulation-environment-for-event-driven-distributed-controls-of-smart-grid.html. 42  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Appendix B. Meter Reading Quality Checks (RQCs) – 2nd Level: Screenshots Low Consumption This check identifies variations in the consumption trend of a particular consumer, with reference to low-threshold values for each References and Links  43 category. High MDI (Maximum Demand Indicator) 44  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities This check identifies variations in the MDI trend of a particular consumer with regard to historical meter reading data and category-wise threshold values. Reading Reversed/Wrong Reading This check identifies erroneous energy consumption readings, such as very high or negative consumption, captured from meters. References and Links  45 Low Power Factor 46  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities This check identifies cases where the power factor value is below the threshold defined by the utility. Zero Consumption This check verifies that previous-month and current-month consumption readings (in kWh) are the same, so that the resultant per-month consumption is zero. References and Links  47 Contract Demand This check compares the demand value from the meter data with contractual demand limits in the consumer master database. A penalty is applied whenever demand exceeds contractual limits. 48  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Non-Reprogramming of Meters This check verifies that the tariff slab information and reset date from the meter data are in line with the consumer master data. References and Links  49 Appendix C. Billing Quality Checks (BQCs) – 3rd Level: Screenshots Current Billed Demand 50  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities This check performs a trend analysis of current billed demand along with a comparison to threshold value. Inflated Bill Inflated Bill This check identifies cases of high bill amount compared to the threshold value for the particular category. References and Links  51 Negative Amount Negative Amount 52  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities This check identifies cases where a customer’s bill amount for the current month is negative. High Slab High Slab This check compares the slab value with the threshold value for particular category of consumer. References and Links  53 Net Metering 54  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Net Metering Billing Annexure CA No. : 60006105302 Name : MANAGER Bill Number : Bill Period : 01/12/2018 to 31/12/2018 Bill Date : 14/01/2019 NET-Meter No. : 93207940 Multiplying Factor : 1.00 Remark : OK Sanctioned Load (KW/KVA) : 47.00/51.00 Solar Meter : 93208748 Solar Consumption : 2170 Solar Meter Sanctioned Load (KWp) : Solar Meter Installation Date : 11/03/2016 Reading Details Time of Day Details Moderation Details Net Meter Units Previous Date Current Date Cumulative Peak Normal Off Total Unit- Unit- Unit-Off Units Carried Details Brought Reading Reading Consumption Peak Peak Normal Peak Forward/Billed Forward Draw from 113295 30/11/18 114775 31/12/2018 1479 197 655 627 TPDDL (A) Injection to 37205 38215 1009 225 743 41 TPDDL (B) Net (A-B) 0 470 -28 -88 586 470 -28 -88 588 470 Disclaimer: 1. Excess units if any, will be adjusted against your next month consumption and excess units at the end of the financial year will be refunded thru Cheque. 2. ‘The amount of Government Subsidy & Electricity Tax charged in Bill towards Net metered consumption is subject to revision after receipt of clarification. This check verifies the correctness of bills for consumers having net metering.14 References and Links  55 14 Net metering (or net energy metering, NEM) is a billing mechanism that credits solar energy system owners for the electricity they add to the grid. Appendix D. Meter Data Exception-Generation Checks: Screenshots Consumption Comparison 14,000 12,260.00 kWh 13,000 12,149.00 kWh 12,000 11,356.00 kWh 10,660.00 kWh 11,000 9,831.00 kWh 10,523.00 kWh 10,000 9,124.00 kWh 9,436.00 kWh 8,781.00 kWh 9,000 8,000 6,740.00 kWh 7,000 6,000 5,327.00 kWh Consumption 5,000 4,000 3,000 2,000 1,000 13.00 kWh 0 11 days 31 days 31 days 31 days 31 days 31 days 31 days 31 days Current 30 days 28 days 30 days 30 days History 1 History 7 History 3 History 4 History 8 History 2 History 5 History 6 History 9 History 11 History 10 Histories 56  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities � Active (I) Total-kWh (CH1) � Reactive (I) kVArh (CH3) � Abs Active - kWh (CH5) � Def. in Active (I)- kWh (CH7) � Active (E) Total-kWh (CH2) � Reactive (E) kVArh (CH4) � Abs Apparent - kVAh (CH6) � Def. in Apparent - kVAh (CH8) This check compares the consumer’s current consumption with historical trend and threshold values. Voltage Failure Average Voltage 320 300 280 260 Voltage failure in one phase 240 220 200 180 160 140 120 Voltage (in V) 100 80 60 40 20 0 02- May 04- May 06- May 08- May 08- May 12- May 14- May 16- May 18- May 20- May 22- May 24- May 26- May 28- May 30- May 01- Jun Wed Fri Sun Tue Thu Sat Mon Wed Fri Sun Tue Thu Sat Mon wed Fri This check verifies that system voltage values are in line with tolerance and threshold values. References and Links  57 Assessed Consumption 90 82.52 kVA 82.34 kVA 80 67.54 kVA HIGH MDI 70 60 54.12 kVA 47.32 kVA 50 41.80 kVA 44.38 kVA 40 Maximum Demand 30 16.54 kVA 20 13.42 kVA 10 3.28 kVA 0 Current History 1 History 2 History 3 History 4 History 5 History 6 History 7 History 8 History 9 Histories � Abs Apparent-IVA (CH4) � Abs Active Total-kW (CH1) � Abs Reactive (Lag)-kVAr (CH2) � Abs Reactive (Lead)-KVAr (CH3) This check verifies whether the calculated load factor is in the predefined threshold load factor range for the particular consumer category. 58  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Data Corruption 11,00,000 9,71,312.00 kVAh 10,00,000 9,00,000 DATA CORRUPTED 8,00,000 7,00,000 6,00,000 5,00,000 Consumption 4,00,000 3,00,000 2,00,000 1,00,000 5,613.00 kVAh 4,427.00 kVAh 5,450.00 kVAh 6,130.00 kVAh 0406.00 kVAh 6,035.00 kVAh 5,185.00 kVAh 7,550.00 kVAh 6,143.00 kVAh 4,666.00 kVAh 6,554.00 kVAh 0 Current History 1 History 2 History 3 History 4 History 5 History 6 History 7 History 8 History 9 History 10 History 11 22 days 31 days 30 days 31 days 28 days 31 days 31 days 30 days 31 days 30 days 31 days 31 days Histories � Active(I) - kWh (CH1) � Active(E) Total - kWh (CH3) � Abs Apparent - kVAh (CH5) � Abs Reactive (Lead) -kVArh (CH7) � Active(E) - kWh (CH2) � Abs Active - kWh (CH4) � Abs Reactive (Lag) - kVArh (CH6) � Active [Del] - NegCur-kWh (CH8) � Def.in Active (I) - kWh (CH9) This check looks for data corruption by comparing the values recorded for MDI/consumption data/digits with the threshold values. References and Links  59 This check identifies cases of NVM failure event flags. NVM (Non-volatile Memory) Failure 60  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Power Failure This check identifies cases of power failure event flags. References and Links  61 Neutral Disturbance Neutral Disturbance 62  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities This check identifies cases of unbalanced neutral current values or unbalanced voltage values. Current Reversal Current Reversal This check verifies whether the value of the active current is negative or not. References and Links  63 This check verifies whether for current value is above defined threshold value. CT Overload CT Overload 64  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Current Missing Current Missing This check identifies cases where the phase current is zero or less than specified threshold value. References and Links  65 Power Factor (PF) 1.1 LOW POWER FACTOR 1 0.9 0.8 0.7 0.58 0.57 0.57 0.58 0.57 0.56 0.56 0.55 0.54 0.55 0.55 0.54 0.54 0.6 0.5 Power Factor 0.4 0.3 0.2 0.1 0 Current History 1 History 2 History 3 History 4 History 5 History 6 History 7 History 8 History 9 History 10 History 11 History 12 Histories � Calculated Avg Imp/Avg Power Factor (Unsigned) 66  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities This check verifies whether the Power Factor (PF) data is within the threshold range defined for low and high PF. Manual Reset This check verifies whether the meter reading reset duration is other than the historical value or predefined value. References and Links  67 Real-Time Clock (RTC) Failure 68  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities This check verifies that the meter clock accuracy is in line with the HES clock time. Internal Ratio This check cross-verifies that the on-site CT ratio is consistent with the consumer master data. References and Links  69 Magnet Tampering 120 100 80 60 40 20 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 AvgCurrent-R (A) AvgCurrent-Y (A) AvgCurrent-B (A) This check identifies cases of magnetic tampering by analyzing consumption pattern variations. 70  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Load Unbalance Load Unbalance This check verifies whether the phase current values in three phases are unequal or vary above the tolerance range in each phase. 38 References and Links  71 39 This check identifies cases of any “cover open” event flags. Open CoverOpen Cover 72  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Potential Missing with Load Running Potential Missing with Load Running This check identities cases where the current value is recorded but with voltage value as zero. References and Links  73 Other Checks Other checks include the following: Missing Data/Data Download Gap Check Identifies instances of data missing from periodic or on-demand polling. Time-of-Day (ToD) Consumption Check Verifies whether the ToD slab consumption has been recorded by the meter for the particular consumer category. Meter Pulse Overflow Check Checks that the number of pulses does not exceed the maximum threshold. Test Mode Check Checks whether the data was collected while the meter was in test mode. Energy Sum Check Calculates the difference between the total cumulative consumption shown in the incoming data and the total consumption of various slots over the same time period. Interval Spike Check Identifies intervals showing high usage relative to surrounding intervals. Unit of Measurement (UoM) Check Ensures that the Unit of Measurement (UoM) of the incoming data matches the UoM specified on meter profile. Usage of Inactive Meters Check Identifies cases of consumption shown for inactive meters. Negative Consumption Check Verifies whether the meter reading of a consumer is lower than the previous meter reading. Reactive Load (kVARh) Check Identifies intervals where reactive load (measured in kVARh) is present and active load (measured in kWh) is not. 74  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Appendix E. MIS Reports: Screenshots Planning and Scheduling Report Reading Base MR Actual Invoicing Bill Month District Portion TNC Cycle Date Date May 16 RC–9 CL CLO9B 918 02 May 16 06 May 16 May 16 RC–9 KP KPO9B 454 02 May 16 06 May 16 May 16 RC–9 MN MNO9B 758 02 May 16 06 May 16 May 16 RC–9 PP PPO9B 668 02 May 16 06 May 16 May 16 RC–9 SN SNO9B 422 02 May 16 06 May 16 May 16 RC–9 MT MTO9B 551 02 May 16 06 May 16 May 16 RC–9 BW BWO9B 2044 02 May 16 06 May 16 May 16 RC–9 MP MPO9B 419 02 May 16 06 May 16 May 16 RC–9 NL NLO9B 3087 02 May 16 06 May 16 May 16 RC–9 RH RHO9B 666 02 May 16 06 May 16 May 16 RC–9 SB SBO9B 525 02 May 16 06 May 16 This report outlines the meter reading sequence for a month. Base Meter Reading Performance Report District Accepted Not Accepted Grand Total Base Performance BD 2,529 69 2598 97.34% BW 4,696 112 4808 97.67% CL 2,868 77 2945 97.39% KP 3,165 36 3201 98.88% MN 4,144 108 4252 97.46% MP 5,542 265 5807 95.44% MT 3,957 142 4099 96.54% NL 4,601 163 4764 96.58% PP 3,673 99 3772 97.38% RH 6,071 207 6278 96.70% SB 8,394 279 8673 96.78% SN 3,578 97 3675 97.36% Grand Total 53,218 1,654 54,872 96.99% This is a summary report that is generated after the initial base meter reading (in a non-AMI scenario) showcasing meter reading status. References and Links  75 Communication Success Rate Report Month MODEM Installed Communicated Communication Status w.r.t. Total Mar ’18 71,115 68,163 95.85% April ’18 71,604 67,620 94.44% May ’18 71,721 66,531 92.76% June ‘18 71,726 65,705 91.61% This report shows success rates for remote meter reading. Follow-Up Meter Reading Report REMARK_BASE INSTALLATION Reading Date_ DEVICE NO_ MR ID_BASE Photo (Y/N)_ kVAh_BASE MRU_BASE KWh_BASE SMRD_ID_ PORTION_ kVA_BASE NO_BASE kW_BASE READING BASE BASE BASE BASE BASE 527 250 MN08B MN08B004 5000003694 51003314 TL 23 Jul 18 Y K13 802 KP01D KP01D007 5000004203 41013941 TL 24 Jul 18 Y S74 828 SN09C SN09C012 5000005971 40275911 ND 23 Jul 18 Y 315 189 SN05C SN05C001 5000011977 41283517 TL 24 Jul 18 Y 254 654 CLO4C CLO4C002 5000012738 40251433 ND 24 Jul 18 Y 254 654 CLO4C CLO4C002 5000012760 43034227 TL 24 Jul 18 Y 831 529 MN08C MN08C002 5000013202 41558813 TL 21 Jul 18 Y K12 798 KP01C KP01C001 5000013221 42058397 PL 23 Jul 18 Y 738 766 BD02E BD02E004 5000014203 41084490 OB 21 Jul 18 Y 392 707 MP02D MP02D002 5000015065 41072411 TL 24 Jul 18 Y 4 139 CL04D CL04D004 5000017332 72285469 TL 23 Jul 18 Y This report gives details (including serial numbers) of meters that have failed RQC validations and thus require follow-up on-site meter readings. 76  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Monitoring Report (Unread Meters) Shalimar Bagh Contractor No Keshavpuram Shakti Nagar Model Town Mangolpuri Moti Nagar Pitampura Civil Lines Bawana Narela Rohini Badli Sum: Sub GNI 1 1 1 3 1 1 8 HRB 89 119 58 61 138 63 92 113 51 92 61 158 1,095 KCG 4 3 1 1 4 2 2 2 1 20 PP-HCB 17 20 12 21 15 16 17 13 10 36 8 16 201 PP-NOTOD 2 1 2 1 2 8 PP-TOD 1 3 1 2 7 RC1 10 1 7 2 21 4 2 2 2 51 RC2 3 3 1 4 19 6 103 139 RC3 2 1 3 5 5 2 3 23,541 23,562 RC9 1 1,980 3 14 25 1 2,918 7 4 79 5,032 SCB 79 15 7 18 2 8,221 2,410 235 1 1 16 11,005 SHORT TE 1 1 SOLR-HCB 9 2 9 5 3 6 7 8 12 10 6 77 STLT 10 19 7 2 6 1 6 218 2 6 2 15 96 Sum: 10,436 11,335 7,060 5,789 15,768 13,088 11,505 10,858 6,380 10,471 5,532 23,943 13,2165 This report shows the number of meters unread due to meter inaccessibility. Monitoring Report (BQC Checks) Responsibility Center 1 day 2-5 days 6-7 days 8-15 days >15 days Grand Total BQC Desk 35 62 5 4 106 JE Authorization Desk 1 1 1 3 Meter Reading Analysis Desk 132 113 7 2 254 Meter Reading Analysis Desk – HRB 3 36 39 Rcg Coordination Desk 2 1 3 Grand Total 173 213 12 6 1 405 This report provides the status of various BQC Checks. References and Links  77 Bill Distribution Report/Disconnection Notice Report Bill Distribution MIS Type of Bill Billing Invoicing Bills Bills Portion Total Total bills Ideal Status Delays (Schedule/ Date Date Receiving Receiving TNC of Received Date of (Completed/ in no of Unscheduled) Dates from Dates from Portion Dates from Submission Pending) days Printer MRG Printer Unscheduled 12.07.2012 09C 43 15.07.2012 Pending 2 Unscheduled 13.07.2012 09C 27 16.07.2012 Pending 1 Schedule 11.07.2012 12.07.2012 13.07.2012 14.07.2012 BD05A 1540 1380 17.07.2012 Pending Schedule 12.07.2012 13.07.2012 14.07.2012 14.07.2012 BD05B 1498 1245 17.07.2012 Pending Unscheduled 14.07.2012 SPL 172+140 17.07.2012 Pending Unscheduled 14.07.2012 AMR 1184 17.07.2012 Pending Unscheduled 14.07.2012 ADDRESS 2+1 17.07.2012 Pending FILE (SPL) Unscheduled 14.07.2012 ADDRESS 4 17.07.2012 Pending FILE (SPL) Schedule 13.07.2012 14.07.2012 16.07.2012 16.07.2012 BD05C 1023 722 19.07.2012 Pending Schedule 13.07.2012 14.07.2012 16.07.2012 16.07.2012 BD06A 1256 1229 19.07.2012 Pending Schedule 13.07.2012 14.07.2012 16.07.2012 16.07.2012 BD07C 2330 2294 19.07.2012 Pending Schedule 13.07.2012 14.07.2012 16.07.2012 16.07.2012 BD07E 1218 1212 19.07.2012 Pending Unscheduled 16.07.2012 SPL 227 19.07.2012 Pending Unscheduled 16.07.2012 AMR 166 19.07.2012 Pending Unscheduled 16.07.2012 ADDRESS 5 19.07.2012 Pending FILE (SPL) This summary report shows the number of bills, disconnection notices distributed (with or without proof of delivery, or PoD) and undistributed bills. 78  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Reconciliation Report Not to Remaining Pending Consider Unbilled Total no. of Total Total consumers Invoiced Active Resolved Resolved Unbilled First Cycle Active Inactive TD DT Total Bill % age % age Unbilled H=(C- (A) (B) (C) (D) (E) (F) (G) (A-H) (F+G)) RC1 180786 149518 3356 26419 1493 17 520 2819 177967 98.44 1.56 RC2 204103 172759 3315 26469 1560 7 714 2594 201509 98.73 1.27 RC3 227228 187619 3656 34056 1921 30 788 2838 224390 98.75 1.25 RC4 193881 161234 4258 26378 2011 19 698 3541 190340 98.17 1.83 RC5 185259 154329 3523 26133 1274 18 502 3003 182256 98.38 1.62 RC6 188700 158160 3310 25647 1583 24 373 2923 185787 98.46 1.54 RC7 212348 176006 4730 29879 1733 22 491 4217 201831 98.01 1.99 RC8 217974 181426 4652 30344 1552 18 629 4005 213969 98.16 1.84 RC9 41993 2131 3485 36084 293 35 245 3205 38788 92.37 7.63 SCB 170875 130116 4266 33947 2546 13 1117 3136 167739 98.16 1.84 G&I 8580 7273 543 609 155 2 46 495 8085 94.23 5.77 HRB 87665 68363 3179 15173 950 0 478 2701 84964 96.92 3.08 KCG 2453 1872 12 547 22 0 1 11 2442 99.55 0.45 Prepaid 4639 4204 202 104 129 0 22 180 4459 96.12 3.88 Streetlight 5029 4164 278 530 57 0 21 257 4772 94.89 5.11 Grand 1931513 1559174 42768 312295 17279 205 6645 35915 1895598 98.14 1.88 Total This report yields statistics on meters read and unread as well as consumers billed and invoiced. Unbilled for over 60/90 Days Report Recent Category Grand Total Remark HCB SCB G&I HRB KCG Prepaid Street Light OK/MM/LR 999 114 24 27 1 1 6 1172 PL/BL/TL/SP 495 30 27 63 78 24 717 Meter Faulty 308 53 5 23 9 16 414 Open MRO 350 19 11 3 20 3 406 NM/DC 219 92 5 25 14 13 368 Plausible 123 15 28 46 116 11 339 OB/EN/NA/MH 82 97 3 35 2 39 258 BQC 119 8 15 24 15 3 184 NP/NR 64 2 1 3 3 73 Billed but not 4 2 10 16 Invoiced AR Case 11 11 Grand Total 2774 432 118 247 1 268 118 3958 This report shows the number of consumers having provisional bills for 60/90 days. References and Links  79 Current Demand and Billed Units Report 01.04.2018 to 30.04.2018 01.04.20187 to 30.04.2017 Variance Comparison Actual Units Actual Units Actual Units Demand (in Demand (in Billed Units Billed Units Billed Units Provisional Provisional Ownership No. of Bills No. of Bills Demand Current Current Current (in MU) (in MU) (in MU) (in MU) Group No. of No. of (in %) (in %) (in %) Crs.) Crs.) Bills Bills HCB 815023 5321 126.79 126.2 83.14 1082017 3649 187.59 185.9 119.49 -32.41 -32.12 -30.42 HRB 70138 5 213.62 206.62 224.74 70402 11 221.22 213.12 217.62 -3.43 -3.05 3.27 EXPR 278 0 56.44 54.94 53.94 246 0 57.34 55.86 51.05 -1.57 -1.66 5.67 KCG 1661 5 45.83 44.23 47.11 1525 0 46.74 45.08 46.84 -1.94 -1.87 0.58 SCB 153423 791 14.39 14.34 7.46 187744 374 15.8 15.7 8.32 -8.96 -8.70 -10.42 Others 2 0 0.04 0.03 0.05 -100.00 -100.00 100.00 Sum: 1040523 6122 457.07 446.33 416.39 1341936 4034 528.73 515.69 443.37 This report shows the total units billed, the amount billed for the current month, and the amount billed for the same month of the previous year, and deviations. Collection Detail Report SCG KCG HRB Consumers HCB Consumers Total (LT) Grand Total Consumers Consumers Net Demand Due Date Received Received Received Received Received Received Payment Payment Payment Payment Payment Payment Subsidy) Demand Demand Demand Demand Demand Subsidy (W/O (W/O 01 Feb 18 0.71 7.2 4.69 4.29 0.6 0.21 5.99 11.99 0.06 9.19 6.06 21.25 02 Feb 18 0.53 5.24 7.99 4.7 0.09 0.22 8.6 10.17 0.44 0.52 9.05 10.69 03 Feb 18 0.17 2.75 4.59 3.58 0.28 0.2 5.04 6.53 0.33 1.44 5.37 7.97 04 Feb 18 0 0.23 0.03 1.06 0 0.03 0.03 1.32 0 0.03 0.03 1.35 05 Feb 18 0.2 3.47 6.7 4.84 0.21 0.25 7.12 8.61 0 0.89 7.12 9.5 06 Feb 18 0.28 3.02 7.87 4.61 0.15 0.29 8.3 7.92 0 0.29 8.3 8.21 This report shows the daily amount paid by various categories of consumers. 80  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Due Date MIS Reports 25.04.2018 20.04.2018 24.04.2018 Row Labels 23.04.2018 16.04.2018 18.04.2018 21.04.2018 19.04.2018 17.04.2018 Badli 2366 118 42 43 791 8 12824 9 5 Bawana 4403 3063 3893 35 100 8 4118 4396 5 Civil Lines 2253 5484 141 58 2639 174 9971 3276 135 Keshavpuram 80 78 32 24 3270 5 8186 81 1 Mangolpuri 13151 16857 15578 1104 116 16 1068 7866 35 Model Town 191 114 205 23 7657 393 14754 9179 5 Moti Nagar 241 109 232 37 12610 766 24394 4063 9 Narela 4901 3914 5978 77 3135 238 10479 3866 9 Pitampura 965 119 2653 56 5282 12 11880 1495 350 Rohini 705 156 109 69 7732 27 9507 11079 5 This report is issued to head cashiers at collection centers to help them with resource planning. Demand and Collection Report MTD – Demand vs Collection for Feb-17 Vs Feb-18 (01-Feb-18 to 28-Feb-18), Amount in Rs. crores Collection Collection LPSC Demand Due Date Wise MTD Collection Efficiency Efficiency Ownership Income (Gross of LPSC) (Net of LPSC) Group 17 Feb 18 Feb 17 18 17 Feb 18 Feb Variation 17 Feb 18 Feb 17 Feb 18 Feb Collection GS Collection GS Feb Feb HRB 223.56 239.66 7% 207.72 0.24 245.33 0.25 93.02% 102.47% 0.36 0.49 92.86% 102.26% HCB 123.44 136.59 11% 96.56 26.83 113.89 27.44 99.96% 103.47% 0.68 0.7 99.41% 102.96% SCB 7.7 8.57 11% 5.8 2.8 6.28 3.11 111.66% 109.54% 0.16 0.13 109.64% 107.98% Total (LT) 354,71 384.82 8% 310.09 29.87 365.5 30.81 95.84% 102.98 1.19 1.33 95.51% 102.64% KCG 93.09 88.6 -5% 97.21 - 106.71 - 104.43% 120.4% 0 0.05 104.43% 120.38% STL 12.17 12.15 0% 12.5 - 16.38 - 102.79% 134.78% - - 102.79% 134.78% Total KCG 105.25 100.75 -4% 109.71 - 123.09 - 102.24% 122.17% 0 0.05 104.29% 122.11% Total 459.96 485.58 6% 419.81 29.87 485.59 30.81 Due Date 97.76% 106.96 1.19 1.39 97.50% 106.68% - - 449.67 519.39 Pending Collection is net of cheque bounce. Demand & Collection extracted from BIW. Total 459.96 485.58 6% Effect of credit/debit JE’s have not been considered in this report. This report shows the amount billed, amount collected (including subsidies, if any) and collection efficiency for various due dates. References and Links  81 Live and Disconnected Arrear Report Live Arrear Comparison DW Arrear Comparison (For LT Segment – PRIN. + LPSC) (For LT Segment – Only Principal) 160 100 142.36 87.07 140 90 79.23 78.19 81.59 78.66 79.82 127.14 80 77.24 78.87 78.98 77.979.06 75.24 120 114.88 126.67 117.17 109.28 70 75.35 75.18 76.51 72.78 74.62 95.79 73.48 100 72.44 74.15 72.85 71.76 88.8 88.26 60 86.51 95.71 79.01 80 82.68 50 70.46 74.47 51.46 40 60 30 40 20 20 10 0 0 Apr may Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar April May June July Aug Sep Oct Nov Dec Jan Feb Mar Arrear LT Segment FY 15–16 Arrear LT Segment FY 16–17 FY 2015–16 FY 2016–17 This report provides data on the number of consumers defaulted/disconnected, their arrears amounts, and ageing. Disconnection Orders Report DO MIS BEING SHARED WITH DISTRICTS Segment DO Generated on 31.02.2017 and Resolved/held @ RRG for Forwarded to dist. Grid for DO calling attempted further working execution Count Arrear (Lac) Count Arrear (Lac) Count Arrear (Lac) HCB 3543 295.23 1100 84.45 2443 210.73 HRB 1035 453.18 404 168.9 631 284.28 Grand Total 4578 748.42 1504 253.35 3074 495.07 This report shows the status of disconnection orders for various categories of consumers. 82  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Billing Status Report Category/Area Details 5% 6% 89% 0% Average 10% 5% 85% 0% Provisional Regular Others 18% 7% 75% 0% 20% 40% 60% 80% 100% This report shows the percentage of total billing done for various billing statuses (average, provisional, regular, others). Amount Billed Per Consumer for Various Billing Statuses Report Amount in Rs. Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Avg 1,542 1,609 1,398 1,381 1,329 1,267 1,258 1,268 1,289 1,265 1,201 1,016 Prov 1,514 1,812 1,425 1,521 1,374 1,460 1,460 1,578 1,452 1,387 1,309 1,167 Reg 3,094 3,747 4,121 4,317 4,410 4,010 4,008 3,798 3,268 2,886 2,672 2,478 Other 4,090 8,956 13,156 1,767 Total 2,796 3,336 3,541 3,603 3,620 3,311 3,309 3,157 2,769 2,486 2,318 2,134 Category/Area Details Avg 1,543 1,560 1,650 1,107 1,309 1,092 1,327 1,134 1,284 1,140 1,293 1,128 Prov 1,296 1,360 1,387 1,571 1,569 1,540 1,642 1,569 1,568 1,433 1,334 1,214 Reg 2,726 3,098 3,709 3,169 2,875 2,610 2,626 2,564 2,332 2,030 1,988 1,853 Other 578 3,085 3,438 2,574 Total 2,532 2,839 3,366 2,817 2,608 2,353 2,404 2,318 2,155 1,893 1,886 1,749 Avg 3,263 3,205 4,756 3,753 3,053 2,288 2,121 2,241 2,104 2,102 2,130 1,952 Prov 2,210 2,268 3,147 2,323 2,639 2,373 2,253 2,287 2,378 2,236 2,149 1,935 Reg 2,833 3,305 3,784 3,742 3,716 3,707 3,557 3,428 2,784 2,487 2,210 2,203 Other 5,155 93 100 549 Total 2,821 3,234 3,803 3,648 3,601 3,522 3,368 3,270 2,711 2,448 2,202 2,176  Under Billing   Over Billing This report provides the total amount billed per consumer for various billing statuses. References and Links  83 Sundry Adjustment Analysis Report 2,500 No of consumers with sundry adjustment 2,000 283 234 1,500 Others 161 1,000 1774 Domestic 1667 Agriculture 500 1040 11 10 6 178 159 26 0 53 67 38 28 25 2 Utility A Utility A Utility B Utility B Utility C Utility C This report provides the number of consumers with sundry adjustments in terms of amount and units of energy. Billing Behaviour Report No of times - Average/Provisional Billing No of consumers 12 11 10 9 8 7 6 5 4 3 2 1 0 12 6% 1% 1% 1% 1% 1% 1% 2% 2% 3% 5% 10% 66% 99,097 11 12% 4% 3% 3% 3% 3% 4% 4% 5% 7% 13% 39% 2,261 10 27% 2% 3% 3% 3% 3% 3% 4% 7% 11% 32% 1,015 9 19% 3% 3% 3% 6% 5% 5% 9% 14% 33% 712 No of times billed 8 13% 2% 3% 3% 6% 8% 11% 14% 41% 589 7 6% 3% 2% 2% 4% 6% 13% 64% 10,445 6 9% 3% 3% 3% 4% 8% 70% 41,52,935 5 31% 9% 6% 6% 10% 38% 23,508 4 30% 8% 8% 11% 44% 10,608 3 39% 9% 11% 41% 8,147 2 38% 9% 53% 15,303 1 49% 51% 11,226 Total 43,35,846 This report shows the number of times consumers of various bill statuses (provisional, average, and so on) have been billed compared to the total bills generated in a year. 84  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Payment Behaviour Report Number of times paid Total 16% 21% 63% Urban 6% 15% 79% Rural 33% 32% 35% Category/Area Details Total 7% 15% 78% Urban 6% 12% 82% Rural 9% 21% 70% Total 4% 20% 76% Urban 3% 17% 80% Rural 6% 26% 68% Non paying 1-3 times 4-6 times This report breaks down consumer payment behavior in terms of annual payment frequency. Billing and Payment Status Report 85% 85% No of payments to No of bills ratio 83% 80% 78% 82% 82% 76% 73% 74% 75% 75% 75% 78% 71% 71% 70% 70% 74% 70% 73% 73% 73% 70% 72% 70% 70% 70% 69% 65% 66% 60% 62% 60% 59% 59% 59% 59% 61% 58% 58% 58% 55% Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Category/Area Details Utility A Utility B Utility C This report compares the total number of bills generated to the total number of payments received in each billing cycle. References and Links  85 Pending Arrears Trend Report Arrear Comparison (in Crs.) Live Arrear Comparison 160 147.15 140 136.08 136.03 127.12 121.83 120 131.61 123.3 118.17 100 85.59 103.88 101.23 78.27 80 74.77 73.09 60 40 20 0 April May June July August Sept Oct Arrears - FY 13-14 Arrears - FY 14-15 Note: A crore (Cr.) denotes ten million (1,00,00,000). This report shows the total amount of pending arrears at the end of each billing cycle. Suspected Defaulter Consumers Report Average Amount NO Less than Greater Greater Greater Grand Total Frequency of Late Payment Amount or equal to than than than Due 5,000 5,000 and 15,000 and 30,000 less than less than 15,000 30,000 Never late 21,907 Occasionally late 5,005 5,602 3,594 6,521 42,647 Frequent late payment 2,714 4,407 2,850 4,253 Regular late 580 1,440 1,129 2,238 19,611 Grand Total 21,907 8,299 11,467 7,573 1,3012 62,258 This report breaks down the number of late-paying consumers by late-payment frequency and amount, which helps in identifying suspected default consumers. 86  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Meter Status Report 0% 3% 87% 10% Category/Area Details Burnt 1% 6% 83% 10% Defective OK 1% 13% 77% 9% Others 0% 20% 40% 60% 80% 100% This report shows the overall status of meter health as of the latest billing cycle. Meter Ageing Status Report • Outer Circle for All Meters • Inner Circle for Non-OK Meter Category/Area 1 Category/Area 2 Category/Area 3 22% 2% 23% 21% 9% 15% 17% 1% 33% 19% 19% 11% 4% 36% 17% 62% 10% 17% 8% 25% 9% 21% 21% 17% 11% 15% 34% 24% 20% 68% 15% 63% 31% 0-5 Years 6-10 Years 11-15 Years 16-20 Years >20 Years This report provides meter ageing status in terms of number of years. References and Links  87 MONTH OF INSTALLATION METER STATUS MAGNET POWER_ VOLTAGE_ CT_OVER CURRENT_ POWER_ NEUTRAL_ ANALYSIS NUMBER _FLAG FACTOR_ FAILURE_ LOAD_ REVERSAL_ FAILURE_ DISTURBANCE FLAG FLAG FLAG FLAG FLAG _FLAG Apr-14 5000773633 NDP06404 OK OK OK OK OK OK OK OK Logic Checks Apr-14 5000774040 51002788 OK OK OK OK OK OK OK OK Apr-14 5000774265 3114932 OK OK OK OK OK OK OK OK Apr-14 5000774316 NDP18424 OK OK OK OK OK OK OK OK Apr-14 5000774465 54002223 OK OK OK OK OK OK OK OK Apr-14 5000774709 NDP05940 NOT OK OK OK OK OK OK OK OK Consumer Apr-14 5000774759 NDP20100 NOT OK OK OK OK OK OK OK OK Installation Apr-14 5000774949 NDP20281 NOT OK OK OK OK OK OK OK OK Number Apr-14 5000776266 70004662 NOT OK OK OK OK OK OK OK OK Logic Checks Apr-14 5000776306 NDP12895 NOT OK OK OK OK OK OK OK OK Status Apr-14 5000776375 NDP17619 OK OK OK OK OK OK OK OK Apr-14 5000776458 51002743 OK OK OK OK OK OK OK OK Apr-14 5000777000 94201256 OK OK OK OK OK OK OK OK Apr-14 5000777385 NDP17335 OK OK OK OK OK OK OK OK Apr-14 5000777598 NDP17101 OK OK OK OK OK OK OK OK Meter Data Validation Checks Consolidated Report Apr-14 5000777698 70001176 OK OK OK OK OK OK OK OK Apr-14 5000777735 NDP16959 NOT OK OK OK OK OK OK OK OK Apr-14 5000778200 NDP11418 OK OK OK OK OK OK OK OK Apr-14 5000778239 NDP17411 NOT OK OK OK OK OK OK OK OK Apr-14 5000778246 NDP11419 OK OK OK OK OK OK OK OK 88  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Apr-14 5000778274 NDP17842 NOT OK OK OK OK OK OK OK OK Consumer Apr-14 5000778377 NDP15179 OK OK OK OK OK OK OK OK Meter Number Apr-14 5000779202 NDP20596 OK OK OK OK OK OK OK OK This report provides the consumer-wise status of exception checks done on meter data. Apr-14 5000780021 NDP19262 OK OK OK OK OK OK OK OK Apr-14 5000780715 NDP15329 OK OK OK OK OK OK OK OK Apr-14 5000780908 NDP05456 NOT OK OK OK OK OK OK OK OK Date Appendix F. AMR Data Analytics – Use Cases: Screenshots These use cases are used to perform short-, medium- and long-term energy forecasting. The short- term forecasting takes into consideration weather-related data for day-ahead purchase of power. This section addresses the following use cases:  Predictive fault analysis and network-asset management  Energy optimization and Volt/VAR control  Predictive maintenance  Network optimization/load balancing  Interconnected feeder analysis  Energization scheme monitoring Predictive Fault Analysis and Network-Asset Management Predictive Fault Analysis and Network-Asset Management - DT Overload Analysis Interval Read Profile : 31 -May-18 (KT030096) 600 480 Line Current Voltage Energy 360 240 Phase-wise Capacity Limit 220 Amp 120 00:15 00:30 00:45 01:00 01:15 01:30 01:45 02.00 02:15 02:30 02:45 03:00 03:15 03:30 03:45 04:00 04:15 04:30 04:45 05:00 05:15 05:30 05:45 06:00 06:15 06:30 06:45 07:00 07:15 07:30 07:45 08:00 08:15 08:30 08:45 09:00 09:15 09:30 09:45 10:00 10:15 10:30 10:45 11:00 11:15 11:30 11:45 12:00 12:15 12:30 12:45 13:00 13:15 13:30 13:45 14:00 14:15 14:30 14:45 15:00 15:15 15:30 15:45 16:00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 18:15 18:30 18:45 19:00 19:15 19:30 20:00 20:15 20:30 20:45 21:00 21:15 21:30 21:45 22:00 22:15 22:30 22:45 23:00 23:15 23:30 23:45 00:00 kWh kVAh Amp-1 Amp-2 Amp-3 V-1 V-2 V-3 DT loading data is analyzed to ensure optimal loading of the DT to avoid failures and manage the life of the asset. Predictive Fault Analysis and Network-Asset Management - DT Unbalancing Analysis Interval Read Profile : 31 -May-18 (KT030172) 20:15 320 Amp-1:259.98 Amp-2:20.69 Amp-3:87.17 240 Line Current Phase-wise Capacity Limit 220 Amp Voltage Energy 160 80 0 00:15 00:30 00:45 01:00 01:15 01:30 01:45 02:00 02:15 02:30 02:45 03:00 03:15 03:30 03:45 04:00 04:15 04:30 04:45 05:00 05:15 05:30 05:45 06:00 06:00 06:15 06:45 07:00 07:15 07:30 07:45 08:00 08:15 08:30 08:45 09:00 09:15 09:30 09:45 10:00 10:15 10:30 10:45 11:00 11:15 11:30 11:45 12:00 12:15 12:30 12:45 13:00 13:15 13:30 13:45 14:00 14:15 14:30 14:45 15:00 15:15 15:30 15:45 16:00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 18:15 18:30 18:45 19:00 19:15 19:30 20:00 20:15 20:30 20:45 21:00 21:15 21:30 21:45 22:00 22:15 22:30 22:45 23:00 23:15 23:30 23:45 00:00 kWh kVAh Amp-1 Amp-2 Amp-3 V-1 V-2 V-3 References and Links  89 DT current phase values are analyzed to avoid asset failures and ensure supply reliability and quality. Predictive Fault Analysis and Network-Asset Management- Outage Detection SSN NIC Smart Meter Outage Monitor ( Last 24 Hrs Outages & 2 Hrs Restoration ) Name Address Meter no KNO Outage from Outage to Duration 2017-08-28 RELIANCE JIO INFOCOME KT027145 210722019839 ONGOING 30 18:31:40 2017-08-28 LALIT KUMAR KT027118 210722017021 ONGOING 30 18:31:40 2017-08-28 PRITAM PAL SINGH KT014338 210722010738 ONGOING 30 18:31:40 2017-08-28 LALIT KUMAR JAIN KT027279 210722019064 ONGOING 30 18:31:40 2017-08-28 DHEMENDRA AGARWAL KT014340 210722013699 ONGOING 30 18:31:40 2017-08-28 NEELAM BATALA KT027281 210722014399 ONGOING 30 18:31:40 2017-08-28 TAHIR HUSSAIN KT014566 210722017612 – – 19:05:47 2017-08-28 TAHIR HUSSAIN KT014566 210722017612 – – 18:56:29 2017-08-28 2017-08-28 SUSHIL KUMAR PANDEY AND ROLEY PANDEY KT027185 210722012371 – 18:26:01 18:43:55 2017-08-28 CHAND PRAKASH JAIN KT027098 210722012361 – 18 18:43:54 2017-08-28 STM USHA NARMADEV KT026839 210722012257 – – 18:43:54 2017-08-28 2017-08-28 RAMESH CHAND KT014799 210722012363 18 18:26:17 18:43:54 2017-08-28 2017-08-28 AJEET SINGH KT027199 210722012370 18 18:26:00 18:43:54 Outage event data is analyzed to identify outage locations easily, thereby initiating faster supply restoration. The data can also be used to identify possible cases of theft at customer locations. 90  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Predictive Fault Analysis and Network - Asset Management - Reliability Indices Analysis Time Period: SAIDI SAIFI Unauthorized Construction Notices Safety KPI Dashboard PADCI ? 2018-19 34.34 37.87 4,966 5.20 SAIDI (Hrs.) SAIFI (Nos.) 60 75 60.19 46.06 46.06 43.05 53.63 40 50 50.67 32.41 34.34 38.92 37.87 27.08 26.56 28.29 31.84 27.55 20 30.58 25 35.22 24.80 24.77 0 0 2014-15 2015-16 2016-17 2017-18 2018-19 2014-15 2015-16 2016-17 2017-18 2018-19 Actual Target Actual Target NCC/’000 Complaints PA Compliance (%) 30 100 25.50 99.29 99.30 99.61 99.60 99.64 22.86 21.28 75 20 50 10 25 0 0 2015-16 2016-17 2017-18 2014-15 2015-16 2016-17 2017-18 2018-19 Reliability indices values are mapped and analyzed to understand the performance of DISCOM in supplying reliable power to consumers, predict fault occurrence based on trend analysis, and also understand network overloading instances. Energy Optimization and Volt/VAR control Time Period: SAIDI SAIFI Unauthorized Construction Notices Safety KPI Dashboard PADCI ? 2018-19 34.34 37.87 4,966 5.20 Reliability Intrruption Outages Month Wise Year Wise Year Wise SAIDI (Hrs.) SAIDI (Hrs.) SAIDI (Hrs.) 30 27.81 60 3 20 40 2 20 18.61 10 1 2.77 8.56 0.59 0.05 0 0 0 Category I Category II Distribution Inter.. Distribution Outa.. STS Interruption STS Outage APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR SAIDI (Hrs.) SAIDI (Hrs.) SAIDI (Hrs.) 6 6 7.5 4.00 6.18 4 3.76 4 5 4.72 2.49 1.92 4.07 2 2 3.43 1.28 0.41 0.63 0.41 2.5 0 0 1.07 -2 -2 0 City Circle Metro Circle Sub urban Ci...Town CircleUrban Circle Badli Rohini Shalimar Bagh 414 503 505 532 Bu Shalimar... References and Links  91 Under this use case, the network voltage fluctuation trend is analyzed and points are identified where reactive power management is required by switching on capacitor banks and compensators. This helps ensure power quality in the network and helps prevent unexpected breakdowns of network components. Predictive Maintenance Under this use case, DT and feeder loading data are analyzed to look for violations above peak value of various asset-performance factors and to assess DT temperature trends and outage trends. This ensures proper loading of assets to prevent failures. The utility opts for condition-based maintenance instead of periodic maintenance. Call Center Efficiency Call Center Efficiency 250 200 150 100 50 0 Quality Score (%) Average Handling Time (in secs) Average Hold Time (in secs) Batch without hands on training Batch with hands on training Here, insights gained from analyzing customer patterns and preferences are used to increase the effectiveness of self-service channels. 92  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Network Optimization / Load Balancing Network Optimization/Load Balancing Network Optimization/Load Balancing - Demand Response Demand response load profile data is analyzed to ensure the network is optimally loaded and balanced. References and Links  93 Network Optimization/Load Balancing - DT Swapping 94  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities DT loading patterns are analyzed to ensure optimal loading of network assets and plan load shifting. Interconnected Feeder Analysis Interconnected Feeder Analysis Under this use case, the load curves of all interconnected feeders on the same time stamp are analyzed for better load-growth estimation. References and Links  95 Energization Scheme Monitoring 96  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities The energization scheme monitoring is kind of a project monitoring tool which shows the status of energization scheme to various stakeholders in a single dashboard. References and Links  97 Appendix G. Billing and Collection Data Analytics Use Cases – Screenshots Footfall Optimization at Payment Centers 98  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Here, analytics are used to optimize the footfall at payment centers, leading to better consumer satisfaction and increased resource efficiency. Predicting Defaulting Consumers Overall percentage DN Trend reduction in Disconnection Notice Trend DN–16% 50000 Total Count 8,375 42,259 No of consumers not 820 40000 35,450 reaching DN-L1 – Jan to Sep 30000 % Improvement 9.79% 20000 13,016 11,501 14,925 14,318 12,829 11,120 10000 0 Jan-Mar Apr-Jun Jul-Sep Total 2016 2017 Arrear – Disconnection Notice 250 196.83 200 153.11 150 100 70.85 63.79 48.09 62.19 58.05 46.96 50 0 Jan-Mar Apr-Jun Jul-Sep Total 2016 2017 Here, analytics are used to predict defaulting consumers, leading to reductions in arrears and improved consumers payment behavior. Consumer Segmentation on Default Payments Value Based Segment Default History Segment Diamond Gold Silver Bronze Copper Grand Total Star 8,109 5,703 7,916 5,150 10,032 36,910 Amateur 2,015 1,480 2,610 1,766 2,382 10,253 Crawlers 53 53 56 37 47 246 Climber 635 493 720 481 413 2,742 Cropper 1,919 1,946 4,141 3,306 4,896 16,308 Strategist 375 395 391 208 110 1,479 Stubborn 1,401 1,219 1,991 1,228 845 6,684 Exception 15 10 31 16 78 150 Grand Total 14,522 11,299 17,956 12,192 18,803 74,772 Analytics are used here to group consumers by default history in order to improve upon the reduction of defaults. References and Links  99 Migration to Digital/Online Payment Payment Type MTD% CFY% LFY% Cash 37.32 41.18 48.83 Analytics are used here to study consumers’ modes of payments so the utility can encourage consumers to shift to online payments (e-payments), thereby reducing Cheque 13.12 11.31 14.78 both payment center costs and payment cycle times. E Payments 49.57 47.52 36.39 Consumer Segmentation on Payment Behavior 100  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Here, consumers’ payment behaviour is studied so the utility can work to improve the behavior and retain good consumers. Theft Analytics Theft Analytics - DT Energy Audit Here, DT energy audit data are analyzed to identify thefts based on load-profile variations in the area considered. References and Links  101 Theft Analytics: AT&C Loss Analysis (1) Fiscal Year: AT&C Loss (with LIP) (%) Billing E ciency (%) Collection E ciency (%) Energy Input (MUs) Bill Collection (Crs) Total Billed Amount (Crs) ? FY 18-19 7.93 92.07 100.01 9,630.29 7,973.90 7,973.30 AT&C Loss (with LIP) (%) Billing E ciency (%) Collection E ciency (%) 10 100 100.25 91.80 91.90 92.07 92.20 92.15 91.23 9.87 100.03 90.41 91.38 100.00 100.20 9 100 8.88 50 100.01 8.59 99.93 8 8.40 99.75 7.93 99.69 99.79 99.80 7.67 0 99.5 7 8 15 16 -17 17- 18 18- 19 20 15 -17 18 19 20 14- 15 16 -17 017-1 18- 19 20 14- 15- 2016 19- 14- 16 15- 2016 17- 18- 19- 20 15- 2016 2 20 19- 20 20 20 20 20 20 20 20 20 20 20 20 Actual Target Actual Target Actual Target Energy Input (MUs) Bill Collection (Crs) Total Billed Amount (Crs) 10k 10k 10k 7,381.18 7,973.90 90,402 9,562 90,630 6,856.97 90,040 7.5k 7.5k 8,423 8,610 7,973 7,393 6,450 6,862 6,936 7,917 5k 5k 6,429.88 6,937.95 5k 2.5k 2.5k 512.01 0 0 0 15 18 19 20 15 16 -17 17- 18 18- 19 20 15 16 17 -18 19 20 14- 16 -17 17- 18- 19- 14- 15- 2016 19- 14- 15- 16- 2017 18- 19- 20 15- 2016 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Actual Target Actual Target Actual Target Here, Aggregate Technical and Commercial (AT&C) loss data and billing and collection efficiency are analyzed to understand the DISCOM’s performance patterns, allowing the DISCOM to take actions to improve in these areas. This analysis also provides insights on theft rates. Theft Analytics: AT&C Loss Analysis (2) AT&C Losses Detail (%) for 2017-18 Month Wise Detail Top Five BUs/Zones for Bottom Five BUs/Zones for MAR -2017-18 MAR-2017-18 9.5 9.33 7.5 50 46.76 41.66 9.02 5.35 5.46 9 8.82 4.58 8.76 5 3.89 8.49 22.32 20.77 19.93 8.5 8.33 25 2.5 1.17 8.45 8.52 8.40 8 8.30 0 7.87 7.92 1 1 2 0 3 7.5 56 57 52 1+52 +130 0 50 130 1 512 53 3 517 513 514 L B P N R N C CT G AR AY V JU SE FE AP DE JA JU NO AU O M M District Wise Detail For BU Wise Detail For Mar-2017-18/BWN Circle Wise Detail for MAR-2017-18 MAR-2017-18/SUB 15 15 13.44 12.51 12.5 12.5 11.52 10 10 41.66 7.64 46.76 7.5 6.43 6.78 6.71 7.5 5 5 5.64 2.5 2.5 20.77 0 0 y tro b wn n Cit Me Su To Ur ba NRL BWN 512 513 521 533 Close Here again, AT&C loss data are analyzed to understand overall AT&C loss variation patterns, allowing the DISCOM to pursue loss-reduction measures. 102  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Theft Analysis Theft Collection Detail (Crs.) for 2018-19 YTD Top Five Zones Month Wise Detail Top Five Zones for JAN-2018-19 6 3 0.075 0.06 4.18 2.15 0.05 4 2 1.86 1.86 0.05 1.52 1.75 0.04 0.04 1.28 1.22 1.30 1.20 1.08 1.06 2 1 0.86 0.025 0.75 0.60 0.54 0.12 0.00 0 2 0 0.0 1 8 4 7 V AY AR G CT C 52 N R N B P L 52 41 51 51 2 1 8 4 7 O JU AU AP DE SE FE JU 52 JA 52 41 51 51 M M O N Circle Wise Detail for JAN-2018-19 0.2 District Wise Detail for JAN-2018-19/METRO 0.06 0.16 0.21 0.15 0.17 0.10 0.28 0.36 0.05 0.03 0.02 0 CITY METRO SUB TOWN URBAN KPM MGP PPR Close Identification of theft instance and analysis on collection data for theft rate reduction and utility performance improvement. Meter Reading and Billing Complaints Analysis Complaint ‘000 Customers 6 5.2 5 4.45 4.48 4.13 4.24 4 3.05 3 2.75 2.38 2.28 2.29 2.08 2.16 2.21 1.87 1.98 1.81 1.77 2 1.52 1.6 1.84 1.21 1.1 1.2 1.13 1.09 1.78 1 1.52 1.48 1.27 1.02 1.11 1.03 0.94 0.88 0.8 0 April May June July Aug Sep Oct Nov Dec Jan Feb March FY 12-13 FY 13-14 FY 14-15 Under this use case, the complaints registered for meter reading and bills generated are analyzed over various time periods. References and Links  103 Provisional Billing Analysis Billing Efficiency Detail for 2018-19 150 Billing Efficiency (%) 92.64 92.20 91.70 91.98 92.06 92.30 92.33 92.39 91.96 92.04 92.34 92.07 100 50 0 APR MAY JUN JULY AUG SEP OCT NOV DEC JAN FEB MAR District Wise Billing Efficiency (%) Zone Wise Billing Efficiency (%) Circle Wise Billing Efficiency (%) for FEB-2018-19/Urban for FEB-2018-19/BDL 150 for FEB-2018-19 150 100 95.05 94.37 88.75 100 94.03 93.42 88.97 94.43 92.91 100 91.71 92.74 94.30 50 50 50 0 0 0 City Metro Sub Town Urban SMB BDL RHN 507 516 581 Close Here, analysis of circle/district/zone billing efficiency is used to reduce AT&C loss at the DISCOM. Meter Fault Analysis Here, meter-reading success data are analyzed to assess the performance of smart meters and the communication network. This also helps identify meter fault rates and instances of theft. Read Success Analysis Over A Period of 7 Days Window No Read: Poor (1-39% Available): 2.2% of population 0.1% of population Moderate (40-79% Available): 0.2% of population Good (90-94% Available): Perfect (100% Available) 1.6% of population Contribution: 95.8% of Population 52,614 Cases Very Good (95-99% Available): 0.0% of population Perfect (100% Available): 95.8% of population Perfect (100% Available) Very Good (95-99% Available) Good (80-94% Available) Moderate (40-79% Available) Poor (1-39% Available) No Read 104  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Read Obtained Till Now for: 05 Jun 18 16000 98.69% 98.96% 97.45% 94.55% 98.89% 98.16% 97.88% 94.39% 96.02% 98.6% 98.16% 14000 3.2 12000 98.06 10000 Overall Read meter % Overall Nonread Meter % 8000 6000 4000 2000 0 A1 A2 A3 A4 A5 A6 B1 B2 B3 B4 B5 Target Meters Read Success References and Links  105 Appendix H. Threshold Values for Exception Generation Single-Phase Meters Table 19: Threshold Values for Exception Generation for Single-Phase Meters Persistence Persistence Time Threshold Value for Threshold Value for Exceptions Time for for Occurrences Occurrence of Events Restoration of Events Restoration ESD/JAMMER Immediate (record 0 hr. 01 min. Immunity up to 50 kilovolts Removal of ESD/jammer only 1 event on first 0 sec. (should (kV) with NIC and logging of signal application and only restore after event >50 kV 1 event for next 1 min.) 1 min. of last application) Magnetic Tamper 0 hr. 2 min. 0 sec. 0 hr. 2 min. >0.5 tesla (T) for permanent <0.5 T for permanent 0 sec. magnet magnet OR OR DC magnetic induction DC magnetic induction >0.2 T <0.2 T OR OR AC magnetic induction AC magnetic induction >10 milliTesla (mT) <10 mT Meter Top Cover Immediate Immediate If meter top cover is opened Not applicable Open Neutral Missing 0 hr. 30 min. 0 sec. 0 hr. 2 min. Battery is used for voltage Voltage >190 V 1.  0 sec. reference: Under tamper condition of neutral missing, meter will perform the fraud energy registration above 500 mA assuming Vref (reference voltage from the battery) and Unity Power Factor (UPF). OR Third CT is used for 2.  voltage reference: Under tamper condition of neutral missing, meter will perform the fraud energy registration above 1 A assuming Vref (from third CT) and Unity Power Factor (UPF). Testing: For checking the meter reliability and accuracy in lab, the meter is tested for 30 minutes with voltage sampled at intervals of 30 seconds and constant current. 106  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Persistence Persistence Time Threshold Value for Threshold Value for Exceptions Time for for Occurrences Occurrence of Events Restoration of Events Restoration Neutral 0 hr. 01 min. 0 sec. 0 hr. 02 min. Voltage >145% of Vref Voltage <145% of Vref Disturbance 0 sec. OR OR Current >10% of base current Current <10% of Ib (Ib) OR OR Frequency >47 Hz Frequency <47 Hz OR OR Frequency <53 Hz Frequency >53 Hz OR DC voltage/signal/pulse/ chopped signal injection Current 0 hr. 10 min. 0 sec. 0 hr. 02 min. In-Ip ≥20% of Ib In –Ip <20% of Ib Mismatch 0 sec. AND In >Ip Low Voltage 0 hr. 30 min. 0 sec. 0 hr. 02 min. Voltage <70% of Vref Voltage >70% of Vref Check 0 sec. AND AND Current >2% Ib Current <2% Ib Outage (Power Power OFF = 0 hr. Power ON = Actual Voltage off Actual Voltage On OFF/ON) 05 min. 0 sec. immediate Overload Overload (If enabled) 0 hr. 30 min. I >120% Imax I<100% Imax 0 sec. Microwave Immediate (record 0 hr. 01 min. Any higher frequency Removal of device only 1 event on first 0 sec. (should magnetic waves, micro application and only restore after waves >10 mT (or mutually one event for next 1 min. of last decided) 1 min.) application) Temperature Rise 0 hr. 30 min. 0 sec. 0 hr. 02 min. Temperature >70˚C Temperature <60˚C 0 sec. Network Immediate Immediate On removal of card On insertion of card interface Card (NIC) removal (communication port) EL WC (earth 0 hr. 30 min. 0 sec. Immediate The difference between The difference between load – whole phase and neutral currents phase and neutral currents current) >6.25% of Ib <6.25% of Ib References and Links  107 Three-Phase Meter Table 20: Threshold Values for Exception Generation for Three-Phase Meters Threshold Value Persistence Time Persistence Time Threshold Value for Exception for Occurrence of for Occurrences for Restoration Restoration of Events Events ESD/Jammer Immediate (record 0 hr. 01 min. 0 sec. Immunity up to 50 kV Removal of ESD/JAMMER only 1 event on first (should restore with NIC and logging of signal application and after 1 min. of last event >50 kV only one event for application) next 1 min) Magnetic Tamper 0 hr. 2 min. 0 sec. 0 hr. 2 min. 0 sec. >0.5 tesla (T) for <0.5 tesla for permanent permanent magnet magnet OR OR DC magnetic induction DC magnetic induction >0.2 T <0.2 T OR OR AC magnetic induction AC magnetic induction >10 mT (of any <10 mT frequency) Meter Top Cover Immediate Immediate If meter top cover is NA Open opened Potential Missing 0 hr. 10 min. 0 sec. 0 hr. 2 min. 0 sec. Voltage <70% of Vref Voltage >80% of Vref AND AND Current >2% Ibasic Current >2% Ibasic Voltage Unbalance 0 hr. 30 min. 0 sec. 0 hr. 2 min. 0 sec. 20% or more between Shall be less than 10% the phases between the phases and AND current >2% Ibasic Current >2% Ibasic CT Open 0 hr. 10 min. 0 sec. 0 hr. 2 min. 0 sec. Ir + Iy + Ib + In ≥10% of Ir + Iy + Ib + In <5% of Ibasic (vector Sum) Ibasic.  AND (vector Sum) Phase current <1% of AND Ibasic with All current Phase current >10% of positive Ibasic with All current positive CT Reversal 0 hr. 30 min. 0 sec. 0 hr. 2 min. 0 sec. Active current negative Active current positive AND I >2% Ibasic CT Bypass 0 hr. 10 min. 0 sec. 0 hr. 2 min. 0 sec. Ir + Iy + Ib + In ≥5% of Ir + Iy + Ib + In <2.5% of Ibasic (vector Sum) Ibasic. AND (vector Sum) Any Phase current AND >10% of Ibasic with All Any Phase current >10% current positive of Ibasic with All current positive 108  Data Analytics for Advanced Metering Infrastructure: A Guidance Note for South Asian Power Utilities Threshold Value Persistence Time Persistence Time Threshold Value for Exception for Occurrence of for Occurrences for Restoration Restoration of Events Events Current Unbalance 0 hr. 30 min. 0 sec. 0 hr. 2 min. 0 sec. Current difference Current difference <20% ≥30% between phases between the phases and and I min 10% of Ibasic I min >5% of Ib Low Power Factor 0 hr. 30 min. 0 sec. 0 hr. 2 min. 0 sec. I >1% of Ib I >1% of Ib AND AND Power Factor ≤ 0.5 in Power Factor ≤0.7 in any phase respective phase Neutral Disturbance 0 hr. 01 min. 0 sec. 0 hr. 2 min. 0 sec. Voltage >145% of Vref Voltage <115% of Vref and Current >10% Ib AND OR Current >10% Ib Frequency <47 Hz AND OR Frequency >47 Hz Frequency >53 Hz OR OR Frequency <53 Hz DC voltage/signal/ pulse/chopped signal injection Outage (Power ON/ 0 hr. 02 min. 0 sec. Immediate Actual Voltage off Actual Voltage On OFF) Over Voltage 0 hr. 30 min. 0 sec. 0 hr. 2 min. 0 sec. Voltage >130% of Vref Voltage <110% of Vref Over Current 0hr. 30min. 0 sec. 0 hr. 2 min. 0 sec. > Preset value (default I <100%Ib value set at 120% Ib) Microwave Presence Immediate (record 0 hr. 01 min. 0 sec. Any higher frequency Removal of device only 1 event on first (should restore magnetic waves, micro application and only after 1 min. of last waves >10 mT (or one event for next application) mutually decided) 1 min.) Temperature Rise 0 hr. 30 min. 0 sec. 0 hr. 02 min. 0 sec. Temperature >70˚C Temperature <60˚C NIC Card Removed Immediate Immediate On removal of card On insertion of card Phase Sequence Immediate Immediate Change of phase Restoration of phase Disturbance sequence sequence References and Links  109 Energy and Extractives Global Practice Group South Asia Region (SAR)