Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting

Abstract

We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to raw or transformed prices. Since the variability of prediction errors across windows of different lengths and across datasets can be substantial, selecting ex-ante one window is risky. Instead, we argue that averaging forecasts across different calibration windows is a robust alternative and introduce a new, well-performing weighting scheme for averaging these forecasts.