Forecasting the short-term demand for electricity – do neural networks stand a better chance?

We address a problem faced by every supplier of electricity, i.e. forecasting the short-term electricity consumption. The introduction of new techniques has often been justified by invoking the nonlinearity of the problem. Our focus is directed to the question of deciding whether the problem is indeed nonlinear. First, we introduce a nonlinear measure of statistical dependence. Second, we analyse the linear and the nonlinear autocorrelation functions of the Czech electric consumption. Third, we compare the predictions of nonlinear models (artificial neural networks) with linear models (of the ARMA type). The correlational analysis suggests that forecasting the short-term evolution of the Czech electric load is primarily a linear problem. This is confirmed by the comparison of the predictions. In the light of this case study, the conditions under which neural networks could be superior to linear models are discussed.