1. Over View Of Meter
Data Analytics
Manoj Kumar Gupta
Neha consultancy services, Bangalore
manoj4k@gmail.com

2. Meter Data Analytic
Meter Data Analytics refers to the
analysis of data emitted by electric
Smart Meters that record
consumption of electric energy.
Smart meters send usage data to the
central head end systems as often as
every minute from each meter whether
installed at a residential or a
commercial or an industrial customer.

3. Analytical Methods
Collecting accurate, timely and relevant data is
the bedrock of any data analytics program, but
the data needs to be put into an appropriate
context to become useful information.
1)Aggregations,
2)correlations,
3) Trending,
4)Exception analysis,
5) forecasting.

4. Analytical Methods
Aggregations
An aggregation is a summary of data using
set criteria. Because smart meter data is
is associated with a metering endpoint, it
can be aggregated in dierent ways to
serve planning purposes. For instance,
the meters connected to individual
transformers can be aggregated together
to identify transformer loading patterns

5. Analytical Methods
Correlations
Correlations identify statistical relationships between
related data that are useful for building predictions.
A basic smart meter correlation is the relationship
between outdoor air temperature and power
consumption. The fact that heat waves causes
spikes in power Consumption is well known fact .
Statistical correlation using time-interval
consumption data makes it possible to build
algorithms that predict the size of demand spikes
using forecast temperature.

6. Analytical Methods
Trending
Trending is one of the most basic forms of
analytics, and it can be an obvious win for
improving customer relations and service
quality using smart meter data. A web
page that shows customers a simple
consumption data trend line can help
them relate power
consumption to household activity.

7. Analytical methods
Exception Analysis
Exceptions are unexpected or abnormal conditions.
A missing meter read, for instance, is an exception
event. The ability to analyze exceptions over time is
valuable for identifying problems in communications
and measurement infrastructure, as well as in the
distribution grid. Equipment failure is useful for
homing in on a subset of data for other forms of
analysis. In the case of a blown transformer, it may
be useful to build a historical trend of transformer
loading prior to the failure. Once pre-failure patterns
are identified, they canbe used to build predictive
algorithms useful for preventing future failures.

8. Analytical Methods
Forecasts
Forecasts are predictions of future
events or values using historical data.
For instance, a forecast of power
consumption for a new residential
subdivision can be created using
historical data from similar homes.
Forecasts can also be built using
correlation data.

9. Analytics Application
Meters data and analytics will
revolutionize the way power is
managed, delivered, andso
Applications:
Revenue Management,
Customer Engagement,
Distribution optimization and
AMI Network Management.

10. Challenges of Meter Data Analytics
Data required for complete meter data analytics
solution does not reside in the same database,
instead, resides in disparate databases among
various departments of utility companies.
Meter Data Analytics need to deal with big data
problem.
Many utility companies do not have
infrastructure to support such needs

11. Neha Consultancy Services
Thank you
manoj4k@gmail.com
https://sites.google.com/site/nehaconsultancyservic
es