ENERGY RESOURCE MANAGEMENT

Through DATA MINING

Project Mentor Prof. Somdeb Lahiri

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

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

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

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.

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

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)

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

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

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

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.

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

Group - 6Arnab Kumar Chatterjee KVNKC Sharma Koustav Naha Riddhima Kartik Sujit Sinha

2015100520151017201510192015103720151042