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James W. Taylor, Lilian M. de Menezes and Patrick E. McSharry (2005) A comparison of univariate methods for forecasting electricity demand up to a day ahead International Journal of Forecasting 22(1): 1-16.

Abstract

This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day-ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro, and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives.

Keywords

electricity demand forecasting; exponential smoothing; principal component analysis; ARIMA; neural networks.