Computing electricity spot price prediction intervals using quantile regression and forecast averaging

We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity spot price forecasts leads to better forecasts than those obtained from individual methods. Next, we propose a new method for constructing PI, which utilizes the concept of quantile regression (QR) and a pool of point forecasts of individual (i.e. not combined) time series models. While the empirical PI from combined forecasts do not provide significant gains, the QR based PI are found to be more accurate than those of the best individual model - the smoothed nonparametric autoregressive model.