1. ¹Universidad de Castilla-La Mancha, Ciudad Real, Spain
2. ²Lancaster University, Lancaster, UK

Correspondence: DJ Pedregal, Escuela Técnica Superior de Ingenieros Industriales, Edificio Politécnico, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain. E-mail: Diego.Pedregal@uclm.es

Received April 2006; Accepted March 2007; Published online 4 July 2007.

Abstract

This paper develops a short-term forecasting system for hourly electricity load demand based on Unobserved Components set up in a State Space framework. The system consists of two options, a univariate model and a non-linear bivariate model that relates demand to temperature. In order to handle the rapidly sampling interval of the data, a multi-rate approach is implemented with models estimated at different frequencies, some of them with 'periodically amplitude modulated' properties. The non-linear relation between demand and temperature is identified via a Data-Based Mechanistic approach and finally implemented by Radial Basis Functions. The models also include signal extraction of daily and weekly components. Both models are tested on the basis of a thorough experiment in which other options, like ARIMA and Artificial Neural Networks are also used. The models proposed compare very favourably with the rest of alternatives in forecasting load demand.