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When One Size No Longer Fits All - Electric Load Forecasting with a Geographic Hierarchy

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 Hakkında

SAS, iş analitiği yazılım ve servislerinde dünya lideri ve iş zekası alanında en büyük bağımsız çözüm sağlayıcıdır. SAS, 75000’den fazla kurulumda, yaratıcı çözümleriyle müşterilerinin performanslarını geliştirmelerine yardımcı olmakta ve daha doğru, hızlı karar vermelerini sağlayarak onlar için değer yaratmaktadır. Kısaca SAS, 1976’dan bu yana dünyanın dört bir tarafındaki müşterilerini ‘BİLMENİN GÜCÜ’ ile tanıştırmaktadır.

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