About this paper
Utility forecasters cannot assume that one methodology will provide the best forecast from one year to the next. To improve forecast performance, reduce uncertainties and generate value in the new data-intensive environment, they must be able to decide which models, or combinations of models, are best. And they must be able to determine more indicators of the factors that affect load. This paper uses a case study to illustrate how utility forecasters can take advantage of hourly or sub-hourly data from millions of smart meters by using new types of forecasting methodologies. It investigates how a number of approaches using geographic hierarchy and weather station data can improve the predictive analytics used to determine future electric usage. It also demonstrates why utilities need to use geographic hierarchies, and why their solutions should allow them to retrain models multiple times each year.
SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW® .
Have a SAS profile? To complete this form automatically: Log In