This slide is related to the use of Data Analytics in the Energy and Power sector. In present scenario Smart Analytics playing vital role from production to distribution of Energy and Power. Also how Suthar Analytics and Data Science services helps to Analyse your Business and increase you Business efficiency.

1. A case Study by ANIL
KUMAR,
Statistical Analyst,
Energy Solution Expert
Analytics in Energy and Power Sector
A part of Suthar Analytics and
Data Science Services

2. Energy products are some of the most volatilecommodity
markets being traded today, Energy Analytics offers utilities
multidimensional support across functional areas and provides
an integratedplatformfor the deployment of business
intelligence. It pushes “actionable intelligence” to all levels of the
organization.

3. Component of Energy and Power
Sector
Generation
Transmission
Distribution
Information Management
Customer and utility

4. Generation
Resource Management.
Forecasting for Demand and
supply.
Power storage and management.
Insure communication among all
Stages.

6. Transformation
Smart Grid and SCADA
Maintain or improve the high level of System
reliability quality, Quality and Security of supply.
Maintain and improve the existing services
efficiency.
Significantly reduce the environmental impact of
Transmission system.
Optimization the performance and minimize the
leakage and Energy loss.

7. Distribution
Smart metering
Route optimization and feasibility
survey
Insure Operational consistency
Customer engagement Solutions
improvement of quality and efficiency
of service

8. Information Management
Smart grid and SCADA Data
management
Collection of Data and Data
management.
Fraud and Loss Prevention.
Privacy and security to protect Data

9. Customer and Utility
Revenue management and utility
optimization
Insure reliable, cost-effective power
Asset and workforce management
Insure availability of Riskless and cost
effective energy

10. The Key Challenges
 What are Energy Price andRisk Management?
 What is Data-Driven Energy Efficiency?
 What is Real-Time Energy Monitoring?
 What is DemandManagement?
 What is an Energy Assessment?
 How to improve efficiency?
 What is energy Audit?
 What is energy supply purchasing?

11. …..Major challenges
 Analyze smart griddata
 AdvancedMetering Infrastructure (AMI), Meter-to-Cash
Analysis
 Customer Usage Pattern Analysis
 Predict oil & gas prices based on historic data
 Identification of fraud/theft
 Service reliability and performance
 Transformeroverload detection and circuit analysis
 And so many more…………

12. How Analytics helps to deal with challenges
Energy DataAnalytics are a crucial aspect of any data-drivenenergy
management plan. Automatedenergy dataanalytics determine discrepancies
between baseline and actual energy data. These energy dataanalytics canalso
benchmarkand compare previous performance withactual energy usage.
Energy dataanalytics helpbusinesses and organizations determine whether or
not their Building ManagementSystem(BMS) is operating efficiently and
hitting the targetedenergy usage goals. They canthen use this datato
investigate areas for improvement or energy efficient upgrades.
Energy dataanalytics unlock savings opportunities and help companies to
understandtheir everyday practices andoperating requirements in a much
more comprehensive manner. Whenenergy dataanalytics are closely monitored,
companies tend to operate more efficiently andwithbetter control over relevant
BMS data.

13. Statistical tools and Analysis approaches
Approaches
 Define the hypothesis based on Objective
 Data collection
 Data cleaning and preparation
 Data Analysis and Modeling
 Result interpretation according to subjective
applications
 Business support and final report.
Tools……

14. Data!!!! On we should focus
Data???
Global energy leadersbelieve – “Energy datais an asset”. The Industrial Process Data is
consideredas a potential productivity “goldmine”.
What Data We should focus in Energy and Power
Analytics!!!!
Energy consumption data at householdand industry level, Voltage,
Frequency and Power Factor data, cost and pricing data, Grid,
location and geography data, Load, Topology & switching history ,
Fault location data, Theft & Frauddata, Environmental data……….

15. Statistical Modeling tools
 Predictive Analytics
 Big Data Analytics
 Reliability and Probabilisticmodeling and estimation
 Market Mix Modeling
 Time Series forecasting and Modeling
 Qualitycontrol
 Stochastic Analysis and Modeling
 Operational ResearchTechniques
 Machine learning Techniques
 AutomationTechniques
 MultivariateAnalysis………..and So manymore…..

16. Software Support
R Language:- Statistical analytical
Software.
C, C++ programming Language
Python , SPSS, SAS, JAVA and other
Analytics software

17. Ready for Analysis!!!!!
Our Data Science platform can supports a
wide range of energy and power solutions
for Smart Grid, Smart Metering, and
Smart Consumer applications in with
“Suthar Data Science and Analytics
Services”.

19. Feel Free to Discuss……..
Suthar Analytics and Data Science
Services,
New Delhi, Mumbai, Bangalore, Jaipur, Hanumangarh
Suthar.analytics@gmail.com
+91-8107763072