Taylor, James and McSharry, Patrick (2007) Short-term Load Forecasting Methods: An Evaluation Based on European Data. IEEE Transactions on Power Systems, 22 (4). pp. 2213-2219.
This paper uses intraday electricity demand data from 10 European countries as the basis of an empirical comparison of univariate methods for prediction up to a day-ahead. A notable feature of the time series is the presence of both an intraweek and an intraday seasonal cycle. The forecasting methods considered in the study include: ARIMA modeling; periodic AR modeling; an extension for double seasonality of Holt-Winters exponential smoothing; a recently proposed alternative exponential smoothing formulation; and a method based on the principal component analysis (PCA) of the daily demand profiles. Our results show a similar ranking of methods across the 10 load series. The results were disappointing for the new alternative exponential smoothing method and for the periodic AR model. The ARIMA and PCA methods performed well, but the method that consistently performed the best was the double seasonal Holt-Winters exponential smoothing method.
|Keywords:||ARIMA; Electricity demand forecasting; Exponential smoothing; Periodic AR; Principal component analysis|
|Centre:||Faculty of Management Science|
|Date Deposited:||05 Feb 2012 15:21|
|Last Modified:||23 Oct 2015 14:06|
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