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Energies 2018, 11(9), 2364; https://doi.org/10.3390/en11092364

Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting

1
Department of Operations Research, Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
2
Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
*
Author to whom correspondence should be addressed.
Received: 17 August 2018 / Revised: 5 September 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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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. View Full-Text
Keywords: electricity price forecasting; forecast averaging; calibration window; autoregression; variance stabilizing transformation; conditional predictive ability electricity price forecasting; forecast averaging; calibration window; autoregression; variance stabilizing transformation; conditional predictive ability
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Marcjasz, G.; Serafin, T.; Weron, R. Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting. Energies 2018, 11, 2364.

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