The Mathematical Institute, University of Oxford, Eprints Archive

A comparison of univariate methods for forecasting electricity demand up to a day ahead

Taylor, J. W. and de Menezes, L. M. and McSharry, P. E. (2006) A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting, 22 (1). pp. 1-16.



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.

Item Type:Article
Uncontrolled Keywords:Electricity demand forecasting; Exponential smoothing; Principal component analysis; ARIMA; Neural networks
Subjects:O - Z > Operations research, mathematical programming
Research Groups:Oxford Centre for Industrial and Applied Mathematics
ID Code:281
Deposited By: Patrick McSharry
Deposited On:18 Sep 2006
Last Modified:29 May 2015 18:19

Repository Staff Only: item control page