Forecasting next-day electricity demand and prices based on functional models

Abstract

Efficient modeling and forecasting of electricity demand and prices is an important issue in competitive electricity markets. This work investigates the forecasting performance of several models for the 1-day-ahead prediction of demand and prices on four electricity markets (APX Power-UK, Nord Pool, PJM and IPEX). All the models are based on two steps: a nonparametric estimation of some deterministic components, followed by the choice of a suitable model for the residual stochastic component. This latter step includes univariate and multivariate as well as parametric and nonparametric models, with particular emphasis on the functional approach, that models the whole daily profile as a single functional observation. More specifically, the models involved are: a linear model and a nonlinear (nonparametric) autoregressive model, a vector autoregressive model and four autoregressive functional specifications. Prediction covers a whole year. Comparisons are based both on descriptive statistics and on statistical tests of equal forecasting accuracy. Though results partly depend on specific markets, a double functional model always proved to be the best- or no different from the best-model, highlighting the effectiveness of the functional approach.