Journal of the Operational Research Society

, Volume 54, Issue 8, pp 799–805

Short-term electricity demand forecasting using double seasonal exponential smoothing

Case-Oriented Paper
  • 101 Downloads

Abstract

This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the demand on the corresponding day of adjacent weeks. There is strong appeal in using a forecasting method that is able to capture both seasonalities. The multiplicative seasonal ARIMA model has been adapted for this purpose. In this paper, we adapt the Holt–Winters exponential smoothing formulation so that it can accommodate two seasonalities. We correct for residual autocorrelation using a simple autoregressive model. The forecasts produced by the new double seasonal Holt–Winters method outperform those from traditional Holt–Winters and from a well-specified multiplicative double seasonal ARIMA model.

Keywords

electricity demand forecasting Holt–Winters exponential smoothing 

Copyright information

© Palgrave Macmillan Ltd 2003

Authors and Affiliations

  1. 1.University of OxfordOxfordUK

Personalised recommendations