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Business Analytics & Data Mining

Business Analytics & Data Mining in the Energy Utilities industry
by

Deotima Gangopadhyay

on 4 March 2014

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Transcript of Business Analytics & Data Mining

Business Analytics
&
Data Mining

Industry: Energy utilities
Company: Pepco

Business Analytics
is the process of iterative, methodical exploration of an organization's data with emphasis on statistical analysis.
Focus on Data-Driven Decision making.
Performed in four types or steps, to answer a series of business-driven questions:
Descriptive Analytics:
"What happened in the past?"
Diagnostic/Decisive Analytics:
"Why did it happen?"
Predictive Analytics:
"What is likely to happen?"
Prescriptive Analytics:
"What should I do about it?"
Example of Analytics in action: Bar deciding a happy hour special.


Data Mining:
The process of analyzing raw data in a number of different ways and dimensions, and summarizing and condensing it into actionable information by looking for clusters, associations or patterns.
Also known as data discovery or knowledge discovery.
Six phases of Data mining:
Problem Definition:
Identify objectives, issues, and requirements
Data Exploration:
Examine metadata for quality and relevance
Data Preparation:
Clean and format data in preparation for modeling
Modeling:
Apply mining functions to data to organize into useful info
Evaluation:
Evaluate the model to see if optimal results have been obtained
Deployment:
Export data into tables, reports, graphs, etc.
Founded in late 1800's as subsidiary of the Washington Traction and Electric Company
Divested itself of streetcar operations in 1935, becoming the Potomac Electric Power Company
Reorganized as a unit of Pepco Holdings, Inc. in 2001
Provides power for nearly 800,000 customers in D.C. and Maryland
Utilities are diversifying their portfolio by investing in distributed renewable generations like wind and solar.

The distribution grid is becoming more dynamic as new technologies are gaining ground. E.g.: customer-owned distributed resources, energy storage, smart metering & electric vehicles charging stations.

Aging infrastructure, unusual weather patterns, cyber security and data privacy threats are challenging the utility's ability to maintain reliability of service.

The installation of new "smart" technologies have provided utilities with tons of new data and made existing data more accessible. What to do with all this data?
Today's consumers expect a rich, tailored "customer experience."

The grid will become more dynamic with increase in renewables like wind and solar, which come on and off the grid. EV charging will become more popular.

As penetration of new technologies grows, there will be an impact on operations, commercial relationships, and distribution infrastructure.

Automated demand response is fast becoming a necessity which, in future, will lead to the development of a distribution marketplace.

Business Analytics & Data Mining will be needed to support this type of market.
Improve their operations and customer satisfaction by:

Protecting revenue (customer)

Improving customer engagement (customer)

Maintaining reliability and securing the grid (operational)

Reducing the cost of operations (operational)

Complying with environmental regulations (operational and customer)
Take advantage of the data to create value for their business by building:

Infrastructure (servers, storage, clustering software, networks, etc.),

Data collection from 'smart' devices (streaming data, monitoring and analysis),

Data management (software that cleanses, normalizes, tags, and integrates data),

Analytics & business intelligence (real-time analysis & automated rules-based transactional decision making),

Decision support (collaboration, scenario evaluation, risk management, and decision capture and retention).
Volume:
Smart grid, smart network devices, SCADA, RTUs, power line sensors, etc are generating more data than ever before.

Variety:
The bulk of the data that Pepco collects through smart meters and grid-generated data, is structured data. It is time series data recorded at uniform time intervals ranging from seconds to hours.

Velocity:
The data collected by the smart meter is collected only periodically. To support the dynamic distribution network of the future, high-velocity data discovery based on interactions between utilities and smart homes and buildings will be required.

Value:
Financial benefit comes from the ability to filter through large amounts of data, unlock insights available through new data sources, and leverage and optimize untapped data.
Customer satisfaction:
Target marketing, evaluate effectiveness of demand response & energy efficiency programs, demand response incentives, analytical tools to help customers reduce their energy bills, communications about outages.

Improving operations:
Pinpoint location of outages, scan for potential security breaches, perform analysis of faults or voltage irregularities.

Fraud detection:
Detect fraud and theft, identify unbilled accounts.

Predictive asset management:
Forecast potential performance or equipment failures by studying load on each asset and rated capacities of devices.

Capital investment planning:
Formulate capital investment strategy by accessing timely information about grid and asset status.
Lack of awareness

Lack of competition

Uncertainty about costs and requirements
Recognize the implications of operating without critical information.

Conduct detailed analysis to determine what new technology and staff investments are required.

Consider cloud services and a shared services model with other utilities to reduce costs.
Recognize value of untapped data in supporting fact-based decisions in any industry. But by itself, this wealth of raw data is useless!

Utilizing analytics and data mining, managers can take esoteric data and transform it into meaningful, actionable information.

Formulate a strategy that includes evaluation of decision makers' requirements, decision processes, new technology, legacy systems, and availability & quality of data.
References:
http://eandt.theiet.org/magazine/2014/01/data-on-demand.cfm
http://www-01.ibm.com/software/data/bigdata/industry-energy.html
http://www.intelligentutility.com/article/13/10/big-data-insights-smart-utilities
http://www.auto-grid.com/technology/the-energy-data-platform
http://www8.hp.com/h20195/v2/GetDocument.aspx?docname=4AA1-5375ENW
http://www.sas.com /analysts/Soft_Grid_2013_2020_Big_Data_Utility_Analytics_Smart_Grid.pdf
http://www.rwe.com/web/cms/mediablob/en/1463742/data/4/blob.jpg
http://financialresults.co.za/2012/eskom_ar2012/fact-sheets/images/value-chain.jpg
http://www.ema.ru/files/flib/54.jpg
http://www.c3dmw.com/IRDC3DMW/images/CloudComputing/DataMining1.jpg
http://www.greentechmedia.com/content/images/articles/Grid-Edge-Taxonomy-01.jpg
Presented by: Deotima Gangopadhyay & Edward Rowe
Full transcript