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Clearview Electric Powers Up Energy Forecasting

Oct. 24, 2013
Clearview Electric selected SAS Energy Forecasting to predict demand and costs.

Today’s volatile electricity market puts added pressure on energy forecasters. Both underestimating and overestimating electricity demand can significantly affect a utility’s bottom line. As deregulated markets opened up in the United States, Clearview Electric in Dallas, Texas, experienced rapid growth in residential customer base and revenue. With its expansion across 10 states, Excel-based forecasting collapsed under the strain of big data. The company selected SAS Energy Forecasting to predict demand and costs. It helps the utility buy the right amount of electricity and price it competitively.

“Forecasting was a time-consuming, manual process that involved downloading large amounts of data from various sites and applying industry trends,” said Derek Campbell, Clearview Electric CFO. “But trends generalize one usage volume for every residential customer, and actual energy usage varies from home to home. Forecast inaccuracy exposed us to significant financial risk during energy procurement. We’ve seen the results of such financial risk force other companies to exit the market. We began our search for analytics that are powerful enough to manage large volumes of complex data.”

“We chose SAS Energy Forecasting to help us predict our load more accurately. Now we procure just the right volume of wholesale electricity,” Campbell continues. “SAS integrates and analyzes individual customer-consumption data, weather data and other data sources. We know we’re getting highly accurate forecasts because SAS compares our specific energy use to our historical forecasts. That’s invaluable information because it drives our cash flow and, ultimately, how we run our business.”

SAS Energy Forecasting improves overall process performance through the inclusion of data management, forecasting and reporting. It handles multiple planning horizons – from days to years – to help suppliers, utilities and co-ops operate more efficiently without hiring additional planning or forecasting staff.

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