Day-3, Mr. Satyajit Dwivedi analytics for decision making
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This document discusses how utilities can use analytics and data from sources like smart meters, SCADA systems, and transaction data to gain insights. It outlines how analytics can help with peak load management, understanding customer consumption behavior, fraud detection, load forecasting, and predictive asset management. The document also discusses the need for utilities to move from just accessing data to gaining insights through data visualization and analytic visualization. Finally, it presents a maturity model for analytics and notes that smart grid pilots can help utilities orchestrate information management and make fact-based decisions using analytics.
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Day-3, Mr. Satyajit Dwivedi analytics for decision making
- 1. Data to Analytics to Insights in a Smart Grid Satyajit Dwivedi Director – Energy and Strategic Initiatives Global Energy Business Unit SAS Institute Inc.
- 2. Copyright © 2012, SAS Institute Inc. All rights reserved. Customer Behavior is evolving and expanding through multiple touch points
- 3. Copyright © 2012, SAS Institute Inc. All rights reserved.
- 4. Copyright © 2012, SAS Institute Inc. All rights reserved. Digital Sensors, Meters, DCS, SCADA, HISTORIAN were the main source of real time data acquisition in this sector. Other enterprise wide transaction & automation systems generate other enterprise data. Volume Variety Velocity will always grow ! There has never been dearth of data in Energy & Utilities Sector
- 5. Copyright © 2012, SAS Institute Inc. All rights reserved.
- 6. Copyright © 2012, SAS Institute Inc. All rights reserved.
- 7. Copyright © 2012, SAS Institute Inc. All rights reserved.
- 8. Copyright © 2013, SAS Insti tute Inc. Al l ri ghts reserv ed. Analytics can be used by utilities to improve understanding of: • Peal Load Management / Demand Response • Customer Segment Consumption Behavior • Fraud and Non-Technical Loss • Load Forecasting and Portfolio Optimization • Smart Meter Deployment Optimization • Predictive Asset / Alarm Analytics IMMENSE
- 9. Copyright © 2013, SAS Insti tute Inc. Al l ri ghts reserv ed. THE DIFFERENCE BETWEEN RAPID INSIGHT AND FAST INFORMATION DATA VISUALIZATION = EXPLORATION ANALYTIC VISUALIZATION = INSIGHTS “The Greatest Value Of A Picture Is When It Forces Us To Notice What We Never Expected To See.” John W. Tukey, Exploratory Data Analysis 1977
- 10. Copyright © 2012, SAS Institute Inc. All rights reserved. Analytics Platform for Smart Grid
- 11. Copyright © 2012, SAS Institute Inc. All rights reserved. Critical to estimate accurately Design optimal pricing Critical to estimate accurately PEAK LOAD MANAGEMENT / DEMAND RESPONSE MANAGEMENT Analytics for Hourly Peak – Daily Peak – Monthly Peak – Yearly Peak Visualization of Analytics
- 12. Copyright © 2012, SAS Institute Inc. All rights reserved. THEFT MANAGEMENT & TAMPER DETECTION Analytics for Visualisation – Anomaly Pattern Detection – Alert – Corrective & Prevent Action Visualization of Analytics
- 13. Copyright © 2012, SAS Institute Inc. All rights reserved. Source: Davenport, Thomas and Harris, Jeanne. “Competing on Analytics: The New Science of Winning” ANALYTICALLY IMPAIRED LOCALIZED ANALYTICS ANALYTICAL ASPIRATIONS ANALYTICAL COMPANY ANALYTICAL COMPETITOR STAGE 1 STAGE 2 STAGE 3 STAGE 4 STAGE 5 Limited interest in analytics by senior management Line-of- business management are driving analytics momentum Senior executives are committed to analytics Resources are aligned to create a broad analytics capability Enterprise- wide analytics capability is under development as a corporate priority Organization is routinely reaping the benefits of enterprise- wide analytics Focus on continuous improvement 5 Stages of “Analytics” Maturity WE ARE HERE Smart Grid Pilots
- 14. Copyright © 2012, SAS Institute Inc. All rights reserved. 14 Smart Grid Pilots For the Paradigm Shift Orchestrate the Information Management for Fact Based Decision With “Analytics”
- 15. Thank You SAS ranked the #1 Smart Grid Analytics and #1 Data Management vendor in the World by Green Tech Media
Notes de l'éditeur
- External Environment: The existing trend towards e-commerce continues with the explosion of aggregators offering energy switching advice and capabilities. Social media is coming to the fore as a contact mechanism. Progressive utilities will mine the data in social media to understand customer sentiment and key influencers.Consumers are now in control:Price and quality information are now easily available to consumersBrand loyalty is reducedHigh availability of choice – in deregulated markets
- The combination of the visualizations, the easy-to-use yet sophisticated analytics and rapid delivery of insights will allow you to:Have faster time to insightEasily share information with key stakeholders Put analytics into the hands of more business users …But, practically speaking, it will allow you to ….(AE: YOU CAN SELECT TO KEEP ALL TOPICS LISTED, AND CORRESPONDING SLIDES BELOW, OR CHOOSE TO FOCUS ON THE ONES THAT ARE RELEVANT FOR YOUR ACCOUNTS)
- Visualization is critical for quickly understanding what is happening in ‘data’ …and seeing things that we haven’t been able to see before. Data visualization AND analytic visualization are valuable.For example, utilities can effectively visualize consumption trends, identify demand patterns, and target customer segments for specific energy efficiency campaigns on the basis of smart meter data. Or visualize load analysis to understand which assets are being over utilized.Data Visualization is a quick way to gain rapid ‘information’ from your data that is often very descriptive in nature. For example, exploration of customer data would show counts related to number of males versus females, number of customers in specific states or geographies, number of sales of boots to men versus women, etc. Using some basic bar charting techniques, you could easily spot some interesting trends….However, Analytic Visualizations provide more interesting details that result in rapid insight and even foresight. For example, an analytic visualization of customer data would show that there is a strong relationship (high correlation) between women and a particular type of boot sold in a specific state. Another analytic visualization would predict the future revenue of boots in a particular geography, and help determine growth.Analytic visualizations are critical for being able to truly gain insight from the data…and ultimately allow you to share reports/dashboards with others that convey more insight and foresight than hindsight.