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• Cognizant 20-20 Insights
How to Generate Greater Value
from Smart Meter Data
By managing and analyzing smart meter event data,
utilities can improve customer experience, grid reliability,
operational efficiency and revenue assurance.
Executive Summary
Utilities have made significant investments in
smart meter roll-out programs and are now
looking for ways to get a return on this investment.
In addition to ROI, regulators are pushing utilities
to show how these investments are helping to
improve operational efficiencies and deliver
enhanced levels of customer service.
Industry-led efforts such as Green Button1 are
utilizing smart meter read data to provide
customers with visibility into their energy usage
data and consumption and billing patterns, as
well as tools for “what-if” scenarios. However, the
other category of data generated by smart meters
— meter events — is a relatively new concept for
utilities, and its true value is largely untapped.
Some utilities in North America are just at the
early adoption stage of gaining insights from
event data.
Event information relayed from smart meters
includes real-time device status, power quality
information and meter status information, all of
which provides a very powerful source of information to improve utilities’ core business processes.
Based on our experience with and observations
of the changing nature of utilities’ industry
cognizant 20-20 insights | april 2012
operations, we believe that information captured
from events can be used to derive useful insights
to vastly improve customer experience, grid reliability, outage management and operational
efficiency. The challenge lies in managing the
high volumes of event data and applying logical
and predictive analytics to it, such as filtration,
association, correlation, factor analysis and
regression, as these are relatively new concepts
for most utilities.
This white paper discusses the numerous logical
and statistical techniques that utilities can utilize
to tap the potential of events information. It also
illustrates how these techniques can be applied
to improve the outage management process
(outage detection, verification and restoration)
and enhance operational efficiency and field crew
optimization.
Meter Event Data:
Beyond Interval Reads
Smart meters are well known for their ability
to provide meter read data at smaller intervals,
such as every 15, 30 or 60 minutes, as well as bidirectional communication and remote operating
capabilities. In addition to these features, smart
meters also generate hundreds of meter events.
An event is information that originates from the
meters’ endpoints and can have several attributes,
including source and proxy information, severity
level and event category. The source is normally
the device that originates the event, while the
proxy is the device responsible for detecting
and communicating the event. Severity levels
include emergency, information, error, warning
and clear. The event category provides information regarding the process to which the event is
related. There are four basic event categories:
• Meter or device status events, such as
“power restore” and “last gasp.”
• Power quality events, such as voltage sag,
swell and high/low voltage alarms.
• Meter or device tamper flags, such as
reverse energy flow.
• Meter hardware information, such as low
battery alarms and battery critical alerts.
Potential Business Areas for
Events Data Insights
Some of the potential business areas where information from meter events can be used to derive
useful business insights are:
Deriving Business Value
By now, many utilities are broadly aware of the
possible areas where they would like to leverage
information from events. However, the real
challenge lies in how to develop the processes
and systems to continuously convert data into
actionable information and then further refine
the models based on the results.
This challenge arises because of the nature of
event data, both status and exception. Event
data is a raw data stream and is also associated
with high volumes because there are hundreds of
events generated for normal operations, as well
as for changed conditions. These events also need
to be validated with other relevant information,
as they basically manifest the conditions of the
network (meter or grid) and also some aspects of
customer behavior.
To manage the above needs, we believe that
utilities need to focus on two key dimensions:
• Systems
to manage large volumes of events
data, both real-time and batch.
• Logical and statistical techniques that will help
identify the right events and correlate with
various conditions, both event- and businessrelated, and, finally, predict the outcomes.
• Customer
experience: Events like last gasp
and power restore, which can identify field
outages and take proactive action even before
the customer calls, as well as alerts and notifications to customers regarding power outages.
• Outage
management: Events to detect
outages at the right device level and create
proactive tickets, as well as “power restore”
to identify nested outages after large-scale
outage restoration.
Key logical and statistical techniques that could
be used include:
• Data
filtering: This refers to the analysis of
events and intelligent filtration of redundant
data based on predefined conditions from
the event data stream. This technique uses
Boolean logic.2 Based on our experience, events
like last gasp and power restore are relayed
multiple times from the smart meters due to
reliability considerations. These kinds of events
have the same event occurrence intervals but
different event insertion times. Hence, in such
cases, duplicate traps could be filtered from
processing using timing conditions.
• Power quality: Events like “voltage sag” and
“voltage swell,” in correlation with other device
status information to proactively identify open
neutrals and flickering lights.
• Revenue
assurance: Events like meter
inversion and reverse energy flow, along
with meter reads to identify power theft and
abnormal usage/demand patterns.
• Association
rules: Algorithms or business
rules to enable the discovery of relationships
between events and other variables. Inputs
received from other systems, such as work
management systems (WMS), customer information systems (CIS) and supervisory control
and data acquisition (SCADA) systems, may be
associated with event information to determine
device-level issues before rolling out to the field
crews. Also, events received from the smart
• Smart
meter network operations and
monitoring: Events and meter ping commands
to identify damaged/defective meters, access
relays and other devices, as well as hardware
events to provide information regarding
device hardware such as battery information,
firmware version, etc.
cognizant 20-20 insights
2
meters can be logically segregated based on
the inputs received from such systems.
• Point-of-detection
algorithms: These algorithms can help develop patterns of their
occurrence, which can help in taking proactive
actions. For instance, time-wise and day-wise
patterns for events can be developed. Further,
filtration criteria can be applied to remove all
patterns caused by electric, communication
or network issues, and then the remaining
patterns can used to explain occurrences of
certain business outcomes, such as outages,
power quality or device tampering.
• Data
clustering: This is an unsupervised
model that uses data similarity to group the
data points. Similar categories of events can
be clustered together, with analysis performed
to extract business value from the clusters of
events. For example, we can identify clusters
among all event types and then develop relationships between outcomes and clusters of
events. Device status, meter tamper and power
quality events can be a cluster to determine
issues such as open neutrals or flickering lights.
• Correlation:
This measures the association
between two variables, while assuming there is
no causal relationship between the two. We can
develop a correlation among various events
and other outcomes to determine future
behavior. For example, correlation between
event type and consumption fluctuation can
help with revenue assurance.
• Factor
analysis: This allows variables to be
grouped into common sub-groups in order to
reduce the number of factors to be initially
analyzed. For example, by performing factor
analysis, we can identify dominating factors
that contribute to events or a set of events or
an outcome.
• Regression: This refers to the statistical rela-
tionship between two random variables to
predict the outcome. Commonly used for forecasting purposes, regression examines the
causal relationship between two variables. An
example is using regression to analyze the
relationship between equipment conditions in
the field, such as a prediction of transformer
failure, based on the demand from meters
associated with it.
analysis and regression will be required to obtain
the correct results.
Improving Outage Management
through Meter Events
Smart meter events such as last gasp and power
restore that provide meter off/on status can be
used for improving outage management. Being
near-real-time, these events have an advantage
over outage information coming from customers
and field staff. Event information generated by
smart meters is raw data with duplicate traps and
high volume due to:
• Momentary outages and restoration-related
events.
• Communication and network interface issuerelated events
• Events due to planned outages, outages at the
lateral, feeder or transformer level, customer
disconnects, etc.
Hence, it is practically not possible for outage
management systems3 to process raw event data
in the same way as they currently process inputs
from SCADA systems, customers and field staff.
Many utilities realized this when they integrated
event information from head end systems (HES)
directly into their outage management systems.
In order to effectively use events data, an event
processing and analytics engine is required.
This engine needs to have the capabilities of
logical filtration based on uniqueness of events,
momentary and existing outages and capabilities
of association based on physical network hierarchies. It also needs to have pattern analysis or
regression capabilities to predict the outages.
A multistage event processing and analytics
framework identifies confirmed cases of outages
that can be passed to the outage management
system for restoration (see Figure 1).
• Stage
1: A set of conditions is used to filter
duplicates from last-gasp events to identify
unique cases of outage events. Such events
are then correlated with power-restore events
to remove the cases of momentary outages
(outages with a duration of less than 60
seconds).
Further, inputs from other systems such as CIS
and WMS are considered to segregate outage
events that have occurred due to existing
planned maintenance, meter exchange or
customer disconnect. The remaining outage
events are considered as realized events.
Usually, more than one technique might be
required to solve the problem. For example, to
develop a relationship between device status
and outage, a combination of correlation, factor
cognizant 20-20 insights
3
Event Processing and Analytics Framework
Stage 1
Stage 2
Stage 3
Event Processing
Probable Outage
Confirmed Outage
Event
Filtration
Event
Realization
Outage
Escalation
Outage
Comparison
Outage
Verification
Outage
Confirmation
Figure 1
• Stage 2: In this stage, the meter-level realized
events from Stage 1 are escalated to a higher
level of device hierarchies (lateral, feeder, transformer, etc.) and compared with other device
inputs using association rules and conditions
to identify an outage incident. These cases of
outage are considered to be probable cases
that need to be tested further.
• Stage 3: During this stage, the probable cases
of outages from Stage 2 are verified using
remote meter ping functionality, and only
confirmed outage incidents results are communicated to the outage management system
for further action.
The event processing and analytics engine
needs to be integrated into the utilities system
landscape, comprising the head end system, CIS,
meter data management (MDM), WMS, distribution automation and SCADA (see Figure 2). This
will enable effective outage management and
crew optimization by focusing on “real” outage
events from smart meters.
The benefits of this approach include:
• Early and accurate outage detection, leading
to improvement in power system reliability
indices such as CAIDI, SAIDI, etc.
• Early
detection of momentary pnd planned
outages to help avoid costly field visits.
• Outage
and restoration verification to avoid
costly field crew movement.
• Improved intelligence due to inputs from applications such as CIS, WMS and SCADA .
Smart Meter Event Processing: Business Context Diagram
Distribution Area Applications
SCADA
Smart
Equipment Data
Field Force
Automation
Feeder
Telemetry Data
Field Work Execution
Head End
System/
Smart Meter
High-Quality
Events Data
Events Data
Real-Time
Status Check
Smart Meter Event
Processing Solution
Customer/
Premise Data
Customer Information
System/Meter Data
Management System
Planned
Outage Data
Work
Management
System
Figure 2
cognizant 20-20 insights
Real-Time
Status Check
4
Outage
Management
System
Cognizant Smart Meter Event
Processing (SMEP) Solution
Our Utilities Practice has designed a smart
meter event processing (SMEP) solution for
improving the outage management process. The
SMEP solution is configurable to meet dynamic
business requirements and is based on multistage
processing and analytics.
Our SMEP solution is designed to provide the
functionality required to process huge volumes of
real-time outage meter events data. The following
are the key features of the SMEP solution:
• Near-real-time processing of a high volume of
meter event data.
• Business rules-based engine to configure the
algorithms and rules to process the events.
• Dynamic and flexible control based on requirements from other utility systems.
• Business
process management to effectively
route and manage events/incidents.
• Integration with other utility applications for
validation, association and correlation.
• Visualization and dashboarding tools.
In addition to the above features, SMEP has been
designed using the event-driven architecture
(EDA). EDA helps orchestrate the generation,
detection and consumption of meter events, as
well as the responses evoked by them. It helps
effectively manage events and communication with various application processes using
messaging (see Figure 3).
Conclusion: From Data to Insights
The concept of leveraging meter events data
to gain business insights is at an early stage.
To effectively convert raw data into meaningful
insights, utilities need to build state-of-the-art
methods in logical and predictive reasoning with
data management capabilities. The theory of integrating and exploiting logical and statistical data
relationships is quite new; most utilities are still
at an early stage of the maturity curve, primarily
reporting on and dashboarding the smart meter
analytics they gather.
Analytics need a combination of sound business
and statistical capabilities, which many utilities
lack. Statistical capabilities include knowledge of
statistical methods, statistical tools such as SAS
and an ability to provide statistical inferences.
Smart Meter Event Processing Solution
Stage 2
Stage 1
Enterprise Service Bus
Head End System
Event Preprocessing
Event
Event
Filtration Refinement
Stage 3
Confirmed Outage
Probable Outage
Outage
Outage
Outage
Verification
Escalation Comparison
Meter
Events
Outage management
system/other
applications
Visualization and
Dashboarding
Database
Event Log Entry
Smart Meter Event Processing Solution
Figure 3
cognizant 20-20 insights
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Hence, utilities need to have a two-pronged
approach. In the short to medium term,
utilities can build solutions largely on logical
techniques where they have sufficient development experience and can leverage vendors and
partners that provide statistical capabilities.
For the longer term, utilities need to take a holistic
approach toward analytics, keeping in mind the
needs of the enterprise and leveraging various
sources of information (not limited to meter read
or event data) based on the assessment of the
current state of process and people skills. They
should consider various approaches, including
building analytics skills through a Center of
Excellence for Analytics or developing collaborative models with vendors specializing in analytics.
Footnotes
1
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 Distribution
System 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 Intelli­gent
Data 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 Statistical
Methods,” University of Pennsylvania.
Peter Yeung and Michael Jung, “Improving Electric Reliability with Smart Meters,” Silverspring
Networks, 2012, http://www.silverspringnet.com/pdfs/whitepapers/SilverSpring-Whitepaper-ImprovingElectric-Reliability-SmartMeters.pdf.
Yan Liu, “Distribution System Outage Information Processing Using Comprehensive Data and
Intelligent Techniques,” Ph.D. dissertation, Michigan Technological University, 2001.
cognizant 20-20 insights
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About the Authors
Dr. Sanjay Gupta is Cognizant’s Director of Consulting within the Energy and Utilities Practice of Cognizant
Business Consulting. He has more than 20 years of global energy and utilities industry experience in
consulting, business development and business operations and has led and executed consulting engagements with several large global customers. Sanjay is also responsible for developing industry solutions
and services, with a focus on smart grid/smart metering, asset optimization, analytics, renewable energy
and operations management. Sanjay holds a doctorate degree in energy and power and a master’s in
engineering. He can be reached at [email protected].
Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant Business
Consulting, with six-plus years of experience providing consulting services in the implementation of
IT 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 numerous
transformation engagements in the areas of process consulting, package evaluation and solution
design for global utilities companies in regulated and de-regulated markets. He can be reached at
[email protected].
About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered in
Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry
and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50
delivery centers worldwide and approximately 137,700 employees as of December 31, 2011, Cognizant is a member of
the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing
and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.
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