Short-Term Load Forecasting Methods: An Evaluation Based on European Data

Abstract

This paper uses intraday electricity demand data from ten 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 in-traweek 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.