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Electrical load forecasting by exponential smoothing with covariates
Author(s) -
Göb Rainer,
Lurz Kristina,
Pievatolo Antonio
Publication year - 2013
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2008
Subject(s) - exponential smoothing , covariate , term (time) , econometrics , smoothing , exponential growth , computer science , statistics , economics , mathematics , physics , quantum mechanics , mathematical analysis
Abstract 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 & Sons, Ltd.