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1. 978-1-4799-5141-3/14/$31.00 ©2014 IEEE
Advanced Metering Infrastructure Analytics
-A Case Study
I S Jha
Director (Projects)
PGCIL, Gurgaon, India
Subir Sen
GM (Smart Grid & EE)
PGCIL, Gurgaon, India
Vineeta Agarwal
DGM (Smart Grid)
PGCIL, Gurgaon, India
Abstract— Advanced Metering Infrastructure
(AMI) is the basic building block for development of
Smart Grid in Distribution System. The main purpose
of AMI is to enable two way communication between
consumer and Smart Grid Control Center of Utility
which involves remote monitoring & control of energy
consumption as well as other parameters in real time.
Meter data analytics play a vital role in AMI system
which helps utility to manage their resources and
business process efficiently. Indigenously developed
meter data analytics such as meter data validation,
energy audit & accounting of distribution transformer,
missing information, peak demand identification,
consumer profile analysis, load forecasting, abnormal
energy pattern analysis etc. which helps utilities
through improved visualization and enhanced
situational awareness. These would also help in
providing better QoS to consumers as well as empower
them for better energy management. This paper
presents several analytics developed on smart meter
data as part of AMI implemented in Puducherry
Smart Grid Pilot Project.
Keywords—Advanced Metering Infrastructure,
Data Concentrator Unit, Distribution Transformer,
Meter Data Acquisition System, Meter Data
Management System
I. INTRODUCTION
The electricity sector is confronted by critical
challenges viz. growing energy demand, high AT&C
losses, concern on reliability & quality of power
supply, fuel constraints and implementation of
environmental policies to combat climate change
etc. [1]. These challenges are leading to recognition
of consumers and utility as smart energy decision-
makers and advancement of energy efficiency in real
time. In this direction, Smart Grid technologies bring
efficiency and sustainability in meeting the growing
electricity demand with reliability and best of the
quality. However, Smart Grid is applicable to all
value chain of power system but in distribution it
plays a vital role.
Advanced Metering Infrastructure (AMI) is the
basic building block of Smart Grid. It is defined as a
system that measure, collect, transfer and analyze
energy usage and communicate with metering
devices. It enables end users to participate in
reducing peak demands and in contributing to
energy management process. Further, meters can
also capture, receive and execute remote commands
like load disconnect/connect.
The main enabling features of an AMI
infrastructure include smart meter, communication
medium, MDAS/MDM, load monitoring, Demand
response, Load control, Tamper detection, Alarm
handling, Real time energy audit, Time of Day
(ToD) tariff etc.
Smart energy meter serves as a gateway between
utility and consumer. Although the basic purpose of
meter is for energy & other parameters
measurement, however smart meters generates lots
of data which enable higher resolution for entire
electricity delivery system. By capturing smart
meter data and converting into actionable point will
improve efficiency of distribution utility and provide
quality of power to consumer. Smart meter data has
an important role in several Smart Grid applications
and enables novel data analytics tasks, such as
energy consumption behavior, tamper detection,
outage management, automated demand response

2. and energy feedback. It also empowers consumers
for better energy management.
Utilities across North America, Europe, Africa
and Asia have implemented Advanced metering
infrastructure as a cost effective way to modernize
their distribution system while enabling consumer
participation in energy management [2]. Various
analytics such as energy consumption pattern
demand response, tamper detection etc. have
benefitted these utilities in cutting non-technical
losses, supporting network optimization and
controlling energy consumption.
This paper presents several analytics developed
indigenously on smart meter data as part of
Advanced Metering Infrastructure (AMI)
implemented in Puducherry Smart Grid Pilot
Project. The aim of the paper is to analyse the
characteristics of data and to provide utility with
actionable information.
II. AMI ARCHITECTURE
In an established AMI system, it is essential to
have a common platform for monitoring as well as
utilization of essential features of AMI Systems.
The key components of AMI are:
A. Smart Energy Meter
Smart Energy Meters act as a source of
information for consumer behaviour pattern, tamper
and load control etc. It comprises of memory to store
information and communication module to transfer
this information to Smart Grid Control Center.
B. Data Concentrator Unit
The data from cluster of smart meters are
aggregated by a data concentrator unit (DCU) and
then send to the Smart Grid Control Centre. It also
sends messages /signals received from the utility /
consumer for a particular/all meters to the intended
recipient.
C. Smart Grid Control Centre
Meter Data Acquisition System (MDAS) and
Servers are located at Smart Grid Control Centre to
perform periodic collection of information from
smart meters. Logics and validation rules are defined
in Meter Data Management System (MDM) to
sanitize the data.
MDAS is a server based meter data head end
system compatible with multiple standard based
protocols as well as proprietary protocols. MDAS
exchange meter data to meter data management
systems coupled with analytics on standard data
exchange model. A typical architecture of the
Advanced Metering System is shown in Fig 1.
Fig. 1. Advanced Metering Infrastructure
III. AMI ANALYTICS: A CASE STUDY
POWERGRID has under taken a pioneering
initiative to develop Smart Grid pilot through open
collaboration at Puducherry. Different manufactures
have provided meters which works on different
communication technology. Data from all meters are
integrated, synthesized and stored for data analysis
and real time monitoring [3].
Smart Meters have been installed at consumer
premises including various distribution transformers
and feeders. These meters work on various
technologies namely PLC, GPRS, RF 2.4 GHz and
RF 865 MHz. Smart meters working on RF/PLC
communicate to Data Concentrator Unit which
transmits the data to meter data acquisition system.
However, Smart Meters working on GPRS
communicate to MDAS directly. MDAS exchange
meter data to meter data management system [4]. All
of these components of AMI are integrated at one
common platform at Smart Grid Control Center at
Puducherry. Virtualized environment at blade server
along with storage is setup at control center for
monitoring real time energy consumption pattern,
other parameters and various alarms associated with

3. it. The utility can use alarm information for
reliability evaluation and failure analysis. Further,
information also enables utility to monitor the
health/availability of devices in AMI infrastructure.
A typical real time availability status of the field
devices such as Smart Meters and DCUs available
through MDAS are shown in Fig. 2
Fig. 2. Status of Field Devices: Smart Meter and DCU
Typical alarms and alerts such as Load Through
Earth Stop Event, Load Reversal Stop Event etc. are
observed as shown in Fig 3.
Fig. 3. Alarms for Meter failure & fault
Data collected at MDAS is used to developed
meter data analytics to identify the exception and
generate lead for carrying out corrective action. Data
analytics helps utilities to perform on-line energy
audit to operate in efficient manner as well as for
better asset management and system planning [5].
The analytics help utility to extract and use the
information embedded in meter data which provides
many information such as:
• Meter Data Validation.
• Tamper and missing information due to
communication failure, meter fault etc.
• Energy Audit & Accounting of Distribution
transformer.
• Peak Demand Identification.
• Consumer profile analysis.
• Forecast and build predictive models for
demand management or demand response
program planning.
• Consumer abnormal pattern analysis.
The above analysis has been done on meter data
collected under Puducherry Smart Grid Pilot Project.
The data is captured hourly for carrying out analysis.
Following analytics are carried out with meter data
as described below.
A. Data Validation
Abnormal and Missing data input creates big
hassle in analysis [6]. So, very first step of analysis
requires fine grained estimation and validation. Data
validation identifies parameters that can go wrong at
the meter/recorder and cause the data collected not
to reflect actual usage. These rules applied to kWh,
kVARh, voltage, current, Pf data. It evaluates the
quality of the data and generates estimate where
errors, overlaps, redundancies and gaps exist. It
estimates interval data based on meter readings by
filling and correcting the missing gaps and errors.
Fig. 4 shows the missed read of some of the meters
on a particular day.
Fig. 4. Missed Read on a Patricular day

4. B. Tamper and Missing Information
Meter events and usage information can help in
understanding an overall picture of what’s
happening with a consumer’s energy usage over
time. This unified view enables to detect energy
theft, meter tampering or equipment problems that
may be affecting service levels. As availability of
supply at consumer end is very crucial, total power
off hours during a day and long outage events was
analyzed to find out time taken to attend the events
or working efficiency of maintenance crew.
Fig. 5 clearly shows consumer meter outage
between 14 and 19 hrs. These analytics also help
utilities to calculate various reliability indices of
distribution system such as MTTR, SAIFI, SAIDI
etc.
Fig. 5. Outage Duration
C. Energy Audit & Accounting
The efficiency of a power system is determined by
the losses involved in the system. All the technical
losses and commercial losses include AT & C
losses, energy theft etc. which needs to be
effectively reduced. To calculate loss at the Feeder
level or distribution transformer level energy audit
analysis at daily, weekly, monthly was carried out as
shown in Fig. 6.
To understand the consumption pattern and to
analyze the abnormal consumption of energy by
individual consumers various analysis like average
percentage loss, maximum, minimum demand of day
in DT was carried out. These essential details
revealed the nature of load and also helped in
forecasting of load.
Fig. 6. A Typical Energy Audit (Intra-Day)
D. Peak Demand
For electricity grid, the critical requirement is to
flatten the load curve by peak-clipping and valley-
filling through tariff incentive/disincentive or
through demand-side management. The advantages
of flattening the load curves are cost saving on
account of additional infrastructure, energy savings
due to reduce grid losses etc.
Aggregate hourly load profiles of any
distribution transformer reveal load peaks and
valleys in a day. Aggregated meter reading of
individual consumer under the same distribution
transformer gives the information of consumer
contributing in peak and valley. The minimum,
maximum, standard deviation can help utility
identify the consumers who are drawing more or less
during peak hours.
Fig. 7. Peak and Off-Peak Patterns
This analysis has been carried out for different
category of consumers to better understand the
consumption pattern of residential, commercial and

5. industrial consumers. These patterns enable utility to
design Time of Day (ToD) tariff. Fig. 7 shows the
peak and off-peak analysis of distribution
transformer. Table 1 shows number of consumer
category wise contributing in peak in a distribution
transformer.
TABLE 1. NO. OF PARTICIPATING CONSUMERS CATEGORY WISE
DURING PEAK
Peak Time
(hr)
Residential Commercial Industrial
[7,8,9,10] 292 15 13
[18,19,20] 216 12 10
E. Consumer Profile Analytics
Daily energy consumption pattern yields
information such as minimum, average and
maximum energy consumption as well as change in
daily energy usage. These information help
consumers in better energy management. A typical
consumption pattern of a consumer on a particular
day collected through smart meter is shown below in
Fig. 8.
Fig. 8. Consumer Profile on a particular day
Further, weekly & monthly analysis of consumer
consumption behavior has helped utilities to identify
the consumption pattern in working days and
holidays and accordingly plan energy requirement
for working days & holidays. Fig. 9 shows the
consumption pattern of consumer during weekends.
Fig. 9. Consumption Pattern during Weekends
F. Load Forecasting
Load Forecasting is essential for defining the
requirements of the distribution network capacity,
scheduling, approximating AT&C losses, estimating
the existing networks capability to transfer
increasing loads and create effective demand
response programs. On the basis of previous energy
pattern of usage, newly pattern can be identified.
G. Abnormal Behavior Identification
By analyzing consumption pattern of consumers,
utilities are able to distinguish between a normal
daily consumption patterns from an abnormal one.
Based on historical data of consumer, the utility can
identify irregular consumption and detect potential
issues. Therefore, daily energy consumption patterns
can be an important variable to monitor and trigger
consumer action.
Fig. 10. Consumer Profile of particular day
Fig. 10 shows the weekly consumer profile of a
particular consumer with different color showing
each day in a week. From the graph, it is evident that

6. unusual consumption took place on Friday i.e., on
others days consumer behaviour was following
average consumption pattern but on Friday its
consumption increased rapidly.
IV. CONCLUSION
Smart Grid technologies have presented new
dimensions in entire electricity delivery system by
providing higher resolution for real time operation as
well as by empowering consumer to take control of
their electricity usages. Analytics of meter data
extracted in different time horizon with different
time resolution is helpful in planning and meeting
energy requirements. Meter Data Analytics will help
utilities to identify challenges in the existing
distribution system and operate in an efficient
manner by enabling energy audit. These analytics
integrated with Management Information System
(MIS) gives insight on evaluating network
performance, growing demand, quality of power
supply etc. to support decision making process. It
enables consumers to view their own consumption
behaviour, which facilities them to control their
energy usage and optimize energy bills. It also
provides an opportunity for consumers to participate
in demand response program and manage the
available resources efficiently.
ACKNOWLEDGEMENT
Authors are thankful to the management of
POWERGRID for granting permission for
presentation of this paper. Views expressed in the
paper are of the authors only and need not
necessarily be that of the organization in which they
belong.
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