Southern California Edison expands forecasting capabilities with SAS®
SAS® Analytics helps utility contain energy procurement costs, minimize forecasting errors
Because energy trading opens with the markets, Southern California Edison (SCE) forecasters and meteorologists are on the job at 4 a.m., running forecasts for weather, short-term load and renewables output. Using SAS® Analytics for utilities, SCE has begun improving its forecasting efficiency by integrating and analyzing vast stores of data in its energy forecasting platform. Today SCE – one of the nation's largest electric utilities, with nearly 5 million customers – is strengthening energy operations and preparing to address new smart grid challenges and opportunities.
With the increase of wind and solar resources and a high penetration of smart meters in the California market, the business of energy forecasting is increasingly complex. The Short-Term Load Forecasting team at SCE creates a larger number and greater variety of forecasts than ever before. However, gathering quality data for analysis is no longer labor-intensive. With SAS, Southern California Edison integrates data from a wide variety of sources and extracts crucial insights for decision making. Model development, data analysis, reporting and visualization are now all accomplished in a single integrated software platform, streamlining the load and price forecasting process.
Analyze and predict demand, prices
"SAS helped us modernize," said Raymond Johnson, Principal Manager of Demand and Price Forecasting at Southern California Edison. "Having more integrated data and models helps us avoid operational errors and speed up the creation of forecasts, which in turn allows us to spend more time reviewing results and making any necessary changes. Since completing the SAS implementation in December 2012, our forecasters now have greater flexibility on improving model performance, reviewing forecasted results and running reports."
Modernizing operations for smart grid
Progress comes fast at Southern California Edison. With millions of smart meters poised to usher in big data, the forecasting team will gain access to smart grid analysis this year. Terabytes of detailed customer data will help the utility further improve short-term forecasts.
"With new volumes of data from smart grid devices and meters come greater opportunities for utilities to modernize their energy planning with analytics," said Alyssa Farrell, Global Manager of Energy and Sustainability Solutions at SAS, the business analytics leader. "Large utilities such as Southern California Edison, as well as small utilities, are applying SAS Analytics to operations, customer service and renewables for smarter planning and better business results."
In this paper, the concept of demand shaping is extended beyond the idea of demand manipulation that is commonly accepted. This broader definition of demand shaping requires the application of multiple technology sets to understand both supply dynamics and demand dynamics. Combining the analysis of supply and demand yields a much more complete answer to the issue of their alignment. The ability to quickly identify a misalignment and rapidly develop a remedial response is crucial to the organization's profitability. Therefore, an organization that develops a demand-shaping competency will have a significant competitive advantage.
The core of any attempt to support demand shaping is the set of analytical engines that allow the supply-and-demand dynamics to be explored. The interaction between supply and demand in real-world supply chain systems is complex. This complexity must be modeled using the tools and techniques of mathematics and statistics. Only then can the optimal supply/demand alignment be achieved and maintained.
This paper presents two methods that will provide organizations with the ability to exercise a more complete approach to demand shaping: scenario analysis and price/revenue optimization. In addition, a technical architecture is presented that demonstrates how to incorporate data acquisition and preparation, advanced analytical techniques, and presentation and reporting of results to achieve optimal supply-and-demand alignment.
Energy and utility leaders around the world rely on the power of SAS to deliver the analysis, forecasts, and energy trading and risk management systems for effective decision making across the enterprise. For more information on advanced forecasting techniques in use at utilities today, read When One Size No Longer Fits All: Electric Load Forecasting with a Geographic Hierarchy.
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