• Cognizant 20-20 InsightsHow to Generate Greater Valuefrom Smart Meter DataBy managing and analyzing smart meter event da...
An event is information that originates from the      Deriving Business Valuemeters’ endpoints and can have several attrib...
meters can be logically segregated based on          analysis and regression will be required to obtain  the inputs receiv...
Event Processing and Analytics Framework                     Stage 1                                    Stage 2           ...
Cognizant Smart Meter Event                                                         In addition to the above features, SME...
Hence, utilities need to have a two-pronged                needs of the enterprise and leveraging variousapproach. In the ...
About the AuthorsDr. Sanjay Gupta is Cognizant’s Director of Consulting within the Energy and Utilities Practice of Cogniz...
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How to Generate Greater Value from Smart Meter Data


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By managing and analyzing smart meter event data, utilities can improve customer experience, grid reliability, operational efficiency and revenue assurance.

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How to Generate Greater Value from Smart Meter Data

  1. 1. • Cognizant 20-20 InsightsHow to Generate Greater Valuefrom Smart Meter DataBy managing and analyzing smart meter event data,utilities can improve customer experience, grid reliability,operational efficiency and revenue assurance. Executive Summary operations, we believe that information captured from events can be used to derive useful insights Utilities have made significant investments in to vastly improve customer experience, grid reli- smart meter roll-out programs and are now ability, outage management and operational looking for ways to get a return on this investment. efficiency. The challenge lies in managing the In addition to ROI, regulators are pushing utilities high volumes of event data and applying logical to show how these investments are helping to and predictive analytics to it, such as filtration, improve operational efficiencies and deliver association, correlation, factor analysis and enhanced levels of customer service. regression, as these are relatively new concepts Industry-led efforts such as Green Button1 are for most utilities. utilizing smart meter read data to provide This white paper discusses the numerous logical customers with visibility into their energy usage and statistical techniques that utilities can utilize data and consumption and billing patterns, as to tap the potential of events information. It also well as tools for “what-if” scenarios. However, the illustrates how these techniques can be applied other category of data generated by smart meters to improve the outage management process — meter events — is a relatively new concept for (outage detection, verification and restoration) utilities, and its true value is largely untapped. and enhance operational efficiency and field crew Some utilities in North America are just at the optimization. early adoption stage of gaining insights from event data. Meter Event Data: Event information relayed from smart meters Beyond Interval Reads includes real-time device status, power quality Smart meters are well known for their ability information and meter status information, all of to provide meter read data at smaller intervals, which provides a very powerful source of informa- such as every 15, 30 or 60 minutes, as well as bi- tion to improve utilities’ core business processes. directional communication and remote operating Based on our experience with and observations capabilities. In addition to these features, smart of the changing nature of utilities’ industry meters also generate hundreds of meter events. cognizant 20-20 insights | april 2012
  2. 2. An event is information that originates from the Deriving Business Valuemeters’ endpoints and can have several attributes, By now, many utilities are broadly aware of theincluding source and proxy information, severity possible areas where they would like to leveragelevel and event category. The source is normally information from events. However, the realthe device that originates the event, while the challenge lies in how to develop the processesproxy is the device responsible for detecting and systems to continuously convert data intoand communicating the event. Severity levels actionable information and then further refineinclude emergency, information, error, warning the models based on the results.and clear. The event category provides informa-tion regarding the process to which the event is This challenge arises because of the nature ofrelated. There are four basic event categories: event data, both status and exception. Event data is a raw data stream and is also associated• Meter or device status events, such as with high volumes because there are hundreds of “power restore” and “last gasp.” events generated for normal operations, as well• Power quality events, such as voltage sag, as for changed conditions. These events also need swell and high/low voltage alarms. to be validated with other relevant information,• Meter or device tamper flags, such as as they basically manifest the conditions of the reverse energy flow. network (meter or grid) and also some aspects of customer behavior.• Meter hardware information, such as low battery alarms and battery critical alerts. To manage the above needs, we believe thatPotential Business Areas for utilities need to focus on two key dimensions:Events Data Insights • Systems to manage large volumes of eventsSome of the potential business areas where infor- data, both real-time and batch.mation from meter events can be used to deriveuseful business insights are: • Logical and statistical techniques that will help identify the right events and correlate with various conditions, both event- and business-• Customer experience: Events like last gasp related, and, finally, predict the outcomes. and power restore, which can identify field outages and take proactive action even before Key logical and statistical techniques that could the customer calls, as well as alerts and notifi- be used include: cations to customers regarding power outages.• Outage management: Events to detect • Data filtering: This refers to the analysis of outages at the right device level and create events and intelligent filtration of redundant proactive tickets, as well as “power restore” data based on predefined conditions from to identify nested outages after large-scale the event data stream. This technique uses outage restoration. Boolean logic.2 Based on our experience, events like last gasp and power restore are relayed• Power quality: Events like “voltage sag” and multiple times from the smart meters due to “voltage swell,” in correlation with other device reliability considerations. These kinds of events status information to proactively identify open have the same event occurrence intervals but neutrals and flickering lights. different event insertion times. Hence, in such• Revenue assurance: Events like meter cases, duplicate traps could be filtered from inversion and reverse energy flow, along processing using timing conditions. with meter reads to identify power theft and abnormal usage/demand patterns. • Association rules: Algorithms or business rules to enable the discovery of relationships• Smart meter network operations and between events and other variables. Inputs monitoring: Events and meter ping commands received from other systems, such as work to identify damaged/defective meters, access management systems (WMS), customer infor- relays and other devices, as well as hardware mation systems (CIS) and supervisory control events to provide information regarding and data acquisition (SCADA) systems, may be device hardware such as battery information, associated with event information to determine firmware version, etc. device-level issues before rolling out to the field crews. Also, events received from the smart cognizant 20-20 insights 2
  3. 3. meters can be logically segregated based on analysis and regression will be required to obtain the inputs received from such systems. the correct results.• Point-of-detection algorithms: These algo- Improving Outage Management rithms can help develop patterns of their through Meter Events occurrence, which can help in taking proactive actions. For instance, time-wise and day-wise Smart meter events such as last gasp and power patterns for events can be developed. Further, restore that provide meter off/on status can be filtration criteria can be applied to remove all used for improving outage management. Being patterns caused by electric, communication near-real-time, these events have an advantage or network issues, and then the remaining over outage information coming from customers patterns can used to explain occurrences of and field staff. Event information generated by certain business outcomes, such as outages, smart meters is raw data with duplicate traps and power quality or device tampering. high volume due to:• Data clustering: This is an unsupervised • Momentary outages and restoration-related model that uses data similarity to group the events. data points. Similar categories of events can • Communication and network interface issue- be clustered together, with analysis performed related events to extract business value from the clusters of events. For example, we can identify clusters • Events due to planned outages, outages at the lateral, feeder or transformer level, customer among all event types and then develop rela- disconnects, etc. tionships between outcomes and clusters of events. Device status, meter tamper and power Hence, it is practically not possible for outage quality events can be a cluster to determine management systems3 to process raw event data issues such as open neutrals or flickering lights. in the same way as they currently process inputs from SCADA systems, customers and field staff.• Correlation: This measures the association Many utilities realized this when they integrated between two variables, while assuming there is event information from head end systems (HES) no causal relationship between the two. We can directly into their outage management systems. develop a correlation among various events and other outcomes to determine future In order to effectively use events data, an event behavior. For example, correlation between processing and analytics engine is required. event type and consumption fluctuation can This engine needs to have the capabilities of help with revenue assurance. logical filtration based on uniqueness of events,• Factor analysis: This allows variables to be momentary and existing outages and capabilities grouped into common sub-groups in order to of association based on physical network hierar- reduce the number of factors to be initially chies. It also needs to have pattern analysis or analyzed. For example, by performing factor regression capabilities to predict the outages. analysis, we can identify dominating factors A multistage event processing and analytics that contribute to events or a set of events or framework identifies confirmed cases of outages an outcome. that can be passed to the outage management• Regression: This refers to the statistical rela- system for restoration (see Figure 1). tionship between two random variables to predict the outcome. Commonly used for fore- • Stage 1: A set of conditions is used to filter duplicates from last-gasp events to identify casting purposes, regression examines the unique cases of outage events. Such events causal relationship between two variables. An are then correlated with power-restore events example is using regression to analyze the to remove the cases of momentary outages relationship between equipment conditions in (outages with a duration of less than 60 the field, such as a prediction of transformer seconds). failure, based on the demand from meters associated with it. Further, inputs from other systems such as CIS and WMS are considered to segregate outageUsually, more than one technique might be events that have occurred due to existingrequired to solve the problem. For example, to planned maintenance, meter exchange ordevelop a relationship between device status customer disconnect. The remaining outageand outage, a combination of correlation, factor events are considered as realized events. cognizant 20-20 insights 3
  4. 4. Event Processing and Analytics Framework Stage 1 Stage 2 Stage 3 Event Processing Probable Outage Confirmed Outage Event Event Outage Outage Outage Outage Filtration Realization Escalation Comparison Verification ConfirmationFigure 1• Stage 2: In this stage, the meter-level realized meter data management (MDM), WMS, distribu- events from Stage 1 are escalated to a higher tion automation and SCADA (see Figure 2). This level of device hierarchies (lateral, feeder, trans- will enable effective outage management and former, etc.) and compared with other device crew optimization by focusing on “real” outage inputs using association rules and conditions events from smart meters. to identify an outage incident. These cases of outage are considered to be probable cases The benefits of this approach include: that need to be tested further. • Early and accurate outage detection, leading• Stage 3: During this stage, the probable cases to improvement in power system reliability of outages from Stage 2 are verified using indices such as CAIDI, SAIDI, etc. remote meter ping functionality, and only • Early detection of momentary pnd planned confirmed outage incidents results are com- outages to help avoid costly field visits. municated to the outage management system for further action. • Outage and restoration verification to avoid costly field crew movement.The event processing and analytics engine • Improved intelligence due to inputs from appli-needs to be integrated into the utilities system cations such as CIS, WMS and SCADA .landscape, comprising the head end system, CIS,Smart Meter Event Processing: Business Context Diagram Distribution Area Applications SCADA Field Force Automation Smart Feeder Equipment Data Telemetry Data Field Work Execution High-Quality Head End Events Data Events Data System/ Smart Meter Event Outage Smart Meter Processing Solution Management Real-Time Real-Time System Status Check Status Check Customer/ Planned Premise Data Outage Data Customer Information Work System/Meter Data Management Management System SystemFigure 2 cognizant 20-20 insights 4
  5. 5. Cognizant Smart Meter Event In addition to the above features, SMEP has beenProcessing (SMEP) Solution designed using the event-driven architecture (EDA). EDA helps orchestrate the generation,Our Utilities Practice has designed a smart detection and consumption of meter events, asmeter event processing (SMEP) solution for well as the responses evoked by them. It helpsimproving the outage management process. The effectively manage events and communica-SMEP solution is configurable to meet dynamic tion with various application processes usingbusiness requirements and is based on multistage messaging (see Figure 3).processing and analytics. Conclusion: From Data to InsightsOur SMEP solution is designed to provide thefunctionality required to process huge volumes of The concept of leveraging meter events datareal-time outage meter events data. The following to gain business insights is at an early stage.are the key features of the SMEP solution: To effectively convert raw data into meaningful insights, utilities need to build state-of-the-art• Near-real-time processing of a high volume of methods in logical and predictive reasoning with meter event data. data management capabilities. The theory of inte-• Business rules-based engine to configure the grating and exploiting logical and statistical data algorithms and rules to process the events. relationships is quite new; most utilities are still• Dynamic and flexible control based on require- at an early stage of the maturity curve, primarily ments from other utility systems. reporting on and dashboarding the smart meter analytics they gather.• Businessprocess management to effectively route and manage events/incidents. Analytics need a combination of sound business• Integration with other utility applications for and statistical capabilities, which many utilities validation, association and correlation. lack. Statistical capabilities include knowledge of• Visualization and dashboarding tools. statistical methods, statistical tools such as SAS and an ability to provide statistical inferences.Smart Meter Event Processing Solution Stage 1 Stage 2 Stage 3 Event Preprocessing Probable Outage Confirmed Outage Event Event Outage Outage Outage Filtration Refinement Escalation Comparison Verification Enterprise Service Bus Head End System Meter Events Outage management system/other applications Visualization and Dashboarding Database Event Log Entry Smart Meter Event Processing SolutionFigure 3 cognizant 20-20 insights 5
  6. 6. Hence, utilities need to have a two-pronged needs of the enterprise and leveraging variousapproach. In the short to medium term, sources of information (not limited to meter readutilities can build solutions largely on logical or event data) based on the assessment of thetechniques where they have sufficient develop- current state of process and people skills. Theyment experience and can leverage vendors and should consider various approaches, includingpartners that provide statistical capabilities. building analytics skills through a Center of Excellence for Analytics or developing collabora-For the longer term, utilities need to take a holistic tive models with vendors specializing in analytics.approach toward analytics, keeping in mind theFootnotes1 Green Button is an industry-led effort in response to a White House call-to-action http://www.greenbuttondata.org/greenabout.html.2 Boolean logic consists of three logical operators: “OR,” “AND” and “NOT” http://booleanlogic.net.3 Outage management systems develop alternate supply plans and create job orders for restoration.References“Electric Power Industry Overview 2007,” U.S. Energy Information Administration,http://www.eia.gov/cneaf/electricity/page/prim2/toc2.html.Deepal Rodrigo, Anil Pahwa and John E. Boyer, “Location of Outage in DistributionSystem Based on Statistical Hypotheses Testing,” IEEE Transactions on Power Delivery,Vol. 11, No. 1, January 1996, p. 546.Deepal Rodrigo, Anil Pahwa and John E. Boyer, “Smart Grid Regional Demonstration Project:Project Narrative,” DOE-FOA-0000036, August 2009.“Deploy Smart Grid in Difficult and Varying Terrain,” Silverspring Networks,http://www.silverspringnet.com/services/mesh-design.html.Doug Micheel, “Smart Grid Implementation: The PHI Story,” Pepco Holdings, Inc.,Presentation to the 2011 GreenGov Symposium, Nov. 2, 2011.“1-210 Single phase Meter,” GE Energy,http://www.geindustrial.com/publibrary/checkout/GEA13391?TN R=Brochures|GEA13391|PDF.“1-210+c SmartMeter,” SmartSynch, http://smartsynch.com/pdf/i-210+c_smartmeter_e.pdf.Krishna Sridharan and Noel N. Schulz, “Outage Management Through AMR Systems Using An Intelli­ ent gData Filter,” IEEE Transactions on Power Delivery, Vol. 16, No. 4, October 2001, pp. 669-675.Lise Getoor and Renee J. Miller, “Collective Information Integration Using Logical and StatisticalMethods,” University of Pennsylvania.Peter Yeung and Michael Jung, “Improving Electric Reliability with Smart Meters,” SilverspringNetworks, 2012, http://www.silverspringnet.com/pdfs/whitepapers/SilverSpring-Whitepaper-Improving-Electric-Reliability-SmartMeters.pdf.Yan Liu, “Distribution System Outage Information Processing Using Comprehensive Data andIntelligent Techniques,” Ph.D. dissertation, Michigan Technological University, 2001. cognizant 20-20 insights 6
  7. 7. About the AuthorsDr. Sanjay Gupta is Cognizant’s Director of Consulting within the Energy and Utilities Practice of CognizantBusiness Consulting. He has more than 20 years of global energy and utilities industry experience inconsulting, business development and business operations and has led and executed consulting engage-ments with several large global customers. Sanjay is also responsible for developing industry solutionsand services, with a focus on smart grid/smart metering, asset optimization, analytics, renewable energyand operations management. Sanjay holds a doctorate degree in energy and power and a master’s inengineering. He can be reached at Sanjay.Gupta@cognizant.com.Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant BusinessConsulting, with six-plus years of experience providing consulting services in the implementation ofIT systems for the utilities industry. He has extensive experience in smart metering infrastructure,smart grid data analytics solutions and enterprise asset management. Ashish has worked on numeroustransformation engagements in the areas of process consulting, package evaluation and solutiondesign for global utilities companies in regulated and de-regulated markets. He can be reached atAshishMohan.Tiwari@cognizant.com.About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered inTeaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industryand business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50delivery centers worldwide and approximately 137,700 employees as of December 31, 2011, Cognizant is a member ofthe NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performingand fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant. World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. 1 Kingdom Street #5/535, Old Mahabalipuram Road Teaneck, NJ 07666 USA Paddington Central Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London W2 6BD Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 20 7297 7600 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 20 7121 0102 Fax: +91 (0) 44 4209 6060 Email: inquiry@cognizant.com Email: infouk@cognizant.com Email: inquiryindia@cognizant.com©­­ Copyright 2012, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by anymeans, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein issubject to change without notice. All other trademarks mentioned herein are the property of their respective owners.