Development of improved adaptive approaches to electricity demand forecasting

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.