Short-run forecasts of electricity loads and peaks

Ramu Ramanathan, Robert Engle, Clive W.J. Granger, Farshid Vahid-Araghi, Casey Brace

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229 Citations (Scopus)

Abstract

This paper reports on the design and implementation of a short-run forecasting model of hourly system loads and an evaluation of the forecast performance. The model was applied to historical data for the Puget Sound Power and Light Company, who did a comparative evaluation of various approaches to forecasting hourly loads, for two years in a row. The results of that evaluation are also presented here. The approach is a multiple regression model, one for each hour of the day (with weekends modelled separately), with a dynamic error structure as well as adaptive adjustments to correct for forecast errors of previous hours. The results show that it has performed extremely well in tightly controlled experiments against a wide range of alternative models. Even when the participants were allowed to revise their models after the first year, many of their models were still unable to equal the performance of the authors' models.

Original languageEnglish
Pages (from-to)161-174
Number of pages14
JournalInternational Journal of Forecasting
Volume13
Issue number2
DOIs
Publication statusPublished - 1 Jan 1997

Keywords

  • Comparative methods
  • Energy forecasting
  • Exponential smoothing
  • Forecasting competitions
  • Regression methods

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