This document discusses using data mining techniques for energy resource management. It outlines objectives like classification, regression, forecasting and anomaly detection. Techniques covered include cluster analysis, classification trees, neural networks, genetic algorithms and Bayesian models. Applications involve meeting strategic objectives, making business/engineering decisions and energy budgets. Challenges with large data include integration, wasted time and disconnects. The document proposes solutions like Extract-Transform-Load, Hadoop and cloud computing. The methodology uses a geographic information system, forecasting engines and an application programming interface to transfer and analyze data.

1. ENERGY
RESOURCE
MANAGEMENT
Through
DATA MINING
Project Mentor
Prof. Somdeb Lahiri

2. Table of Contents
 Introduction
 Data Mining
 Objective
 Data Mining Techniques related to Energy resource management
 Cluster Analysis
 Classification Tree
 Neural Networks
 Genetic Algorithm
 Bayesian Model
 Application
 Learning & Future Scopes
 References & Citing

3. Data Mining
 Exploration and analysis, by automatic or semi automatic means of large quantities
of data in order to discover meaningful patterns
 Data Mining is required
 To find the data needed
 To identify the proper data required
 To understand the data acquired
 To use the data accumulated
 Steps of Data Mining
 Data Integration
 Data Selection
 Data Cleaning
 Data Transformation
 Data Mining
 Pattern Evaluation
 Knowledge Presentation

4. Objective
 Data Mining Techniques help in solving energy management
problems such as:
1. Classification – Determining consumer profiles based on different
variables, determining the possibility of purchase and install special
equipment for renewable energy generation, based on user profile.
2. Regression – Time trend analysis of the energy consumption and
production, monitoring the effect of energy policies and measures.
3. Forecast – Predicting future energy production and consumption.
4. Anomaly detection – Consumer fraud detection, network intrusion and
other unusual and rare events that are hard to find.
5. Motif discovery – Identify energy relationships that can aid in the
process of forecasting, identify patterns that can be used to predict
customers behavior.
6. Association rules – Analysing the links between certain factors that
could cause increased/decreased energy consumption or production
7. Clustering – Locating fraud or high energy consumption, getting a
group of them around certain areas

5. Data Mining Techniques
 Cluster Analysis
 Data objects with certain characteristics are grouped into clusters based on their
similarities.
 It is one of the two most common Data Mining techniques in relation to the discovery of
hidden patterns in Big Data
 Classification Tree
 Decision trees are used to extract the rule base to enable constructing a fuzzy system for
estimating an demand function
 Neural Network
 Neural networks are powerful nonparametric models which are capable of machine
learning and pattern recognition.
 Genetic Algorithm
 GA is a relatively new component that is considered artificially intelligent and is a popular
choice for finding solutions to complex search and optimization problems
 Bayesian Model
 The Bayesian method was mainly adopted for classifying purposes.

6. Application
 Data is Important because :
 Helps in meeting strategic objectives
 Basis of Business Decisions
 Engineering Decisions
 Energy Budgets etc.....
 Data holds Key to proving you with useful insights into energy usage of your
process
 Can help with operational planning , short term planning and strategic
Planning

7. Big data Challenges
 Report Generation
 Due to large amount of Data; Data Integration and Segregation becomes
tough
 Wastage of Time
 Large data shall always be tough for collection, that leads to wastage of
time
 Spreadsheet Hell
 Including all the collected data into spreadsheet, becomes a tough job,
when the data is huge, as it calls for large calculations also.
 The O&M Disconnect
 At times, there is a short disconnect, due to the non availability of
resources on the right time, so that may create a gap in the data collection
(specially rural areas)

8. Solar Sector
 The increasing penetration of solar electricity gives rise to a number
of simultaneous challenges
 The need for balancing demand and supply
 In turn, May lead to advanced tariff setting systems
 That shall Go hand in hand with advanced monitoring and forecasting of
solar electricity generation.
 Moreover in order to design the right investment program for the
electricity grid infrastructure
 Advanced forecasting of future new installed capacity is needed.
 A Big Data approach of large connected data sets can contribute in
addressing these challenges

9. Possible solutions
 ETL
 Websphere data stage, SSIS
 Data platform
 Hadoop
 Advance analytics
 Cloud computing
 Smart grid.
 Demand and Supply Side
 E & I expert
 Requirement for Technical Aspects

10. Methodology
 A Geographical Information System (GIS)
has been opted for handling the data
analysis and presentation of results.
 Locus built forecasting engine.
 Space-Time Insight
 Next an interface for data transfer from
different systems to a common database for
data storage.
 For this we chose to set up an Application
Programming Interface (API), which
would interact among the systems and
could access databases and transfer the
required data.
 The Hadoop distributed file system.
 Then data analysis in terms of demand-
supply mapping and study has been
conducted.
 Solar Pulse

11. Learning and Future Scope
 Smart grid and big data technology can be used in different renewable
sectors , it is currently only limited to Solar and Wind energy sector.
 In Ocean energy sector and nuclear energy sector Big data can be used
as Ocean energy sector is not fully utilized due to lack of utilization of
current opportunity and for the Nuclear sector it is because of high tariffs
of the energy.
 Utilization of Indian scenario as there is only 13 pilot projects are running
which are not fully operational.

12. References
 Janina POPEANGĂ; Data Mining Smart Energy Time Series; Database
Systems Journal vol. VI, no. 1/2015
 Bikash Sharma; Applications of Data Mining in the Management of
Performance and Power in Data Centers
 Dinesh Rajan, Christian Poellabauer, Nitesh Chawla; Resource Access
Pattern Mining for Dynamic Energy Management
 Maria Riveiro, Ronnie Johansson and Alexander Karlsson; Modeling and
analysis of energy data: state-of-the-art and practical results from an
application scenario

13. Group - 6
Arnab Kumar Chatterjee
KVNKC Sharma
Koustav Naha
Riddhima Kartik
Sujit Sinha
20151005
20151017
20151019
20151037
20151042