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.
Highlights
Over the last three decades the electricity price forecasting (EPF) literature has focused on selecting explanatory variables and developing better performing statistical or computational intelligence models [1]
We have evaluated the results in terms of the root mean squared error (RMSE) and observed only slight differences, e.g., for the ARX2(PJM, ID) model and calibration window sets containing the shortest windows WAW was slightly outperformed by AW averaging
We report on a comprehensive empirical study on the selection of calibration windows for day-ahead EPF
Summary
Over the last three decades the electricity price forecasting (EPF) literature has focused on selecting explanatory variables and developing better performing statistical or computational intelligence models [1] Somewhat surprisingly, it has almost completely ignored the problem of finding the optimal length of the calibration window. Fezzi and Mosetti [29] propose a simple, two-step approach that uses the first step to determine the optimal window length (ranging from only a few to 350 days) for each model and the second step to compare forecasting capabilities across models They argue that improvements over selecting ex-ante one window of ‘typical’ size are significant for the considered datasets from the Nordic and Italian markets. Using data from the Global Energy Forecasting Competition 2014 [31], they show that this kind of averaging yields better results than selecting ex-ante only one ‘optimal’ window length
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