Electrical load forecasting by exponential smoothing with covariates. (9th December 2013)

• Record Type: Journal Article
• Authors: Göb, Rainer; Lurz, Kristina; Pievatolo, Antonio
• Is Part Of: Applied stochastic models in business and industry. Volume 29:Number 6(2013:Nov./Dec.)
• Page Start: 629
• Page End: 645
• Publication Date: 2013-12-09
• Subjects: Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2008
• Languages: English
• ISSNs: 1524-1904

Abstract

<abstract abstract-type="main" id="asmb2008-abs-0001"> <title>Abstract</title> <p id="asmb2008-para-0001">In the past, studies in short‐term electrical load forecasting have been rather sceptical on the use of meteorological covariates like temperature for short‐term forecasting purposes. The main reasons were time delays in data provision and the poor precision of meteorological forecasts. Both arguments have lost their impact, as new recent studies have shown. We explore the use of meteorological covariates in short‐term load forecasting based on the rather new method of exponential smoothing with covariates (ESCov). The existing ESCov model is refined by including multiple seasonalities. The method is empirically explored in the hourly prediction of the electrical consumption of customers from provinces of an Italian region. Copyright © 2013 John Wiley &amp; Sons, Ltd.</p> </abstract>