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.
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Acknowledgements
We are grateful to Shanti Majithia, Chris Rogers, and Sal Sabbagh of National Grid for supplying data and information regarding the company's online demand forecasting. We are also grateful for the useful comments of the anonymous referees.
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Taylor, J. Short-term electricity demand forecasting using double seasonal exponential smoothing. J Oper Res Soc 54, 799–805 (2003). https://doi.org/10.1057/palgrave.jors.2601589
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DOI: https://doi.org/10.1057/palgrave.jors.2601589