Forecasting next-day electricity demand and price using nonparametric functional methods

Abstract

One-day-ahead forecasting of electricity demand and price is an important issue in competitive electric power markets. Prediction task has been studied in previous works using, for instance, ARIMA models, dynamic regression and neural networks. This paper provides two new methods to address these two prediction setups. They are based on using nonparametric regression techniques with functional explanatory data and a semi-functional partial linear model. Results of these methods for the electricity market of mainland Spain, in years 2008–2009, are reported. The new forecasting functional methods are compared with a naïve method and with ARIMA forecasts.