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

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.

► We use functional data nonparametric techniques for electricity demand and price forecast. ► We compare our predictions with those from both ARIMA models and a naïve approach. ► The performance of our model is very competitive both for demand and price forecasting.