Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing

Taylor, James (2003) Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing. Journal of the Operational Research Society, 54 (8). pp. 799-805.


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.

Item Type: Article
Keywords: Electricity; Demand; Time series; Forecasting techniques; Mathematical models
Subject(s): Management science
Date Deposited: 24 Jan 2012 20:09
Last Modified: 10 Feb 2017 16:57
Funders: N/A

Actions (login required)

Edit View Edit View