Selection of calibration windows for day-ahead electricity price forecasting

Grzegorz Marcjasz, Tomasz Serafin and Rafał Weron

No HSC/18/06, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves 6-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.

Keywords: Electricity price forecasting; Forecast averaging; Calibration window; Autoregression; Variance stabilizing transformation; Conditional predictive ability (search for similar items in EconPapers)
JEL-codes: C14 C22 C51 C53 Q47 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2018-08-29
New Economics Papers: this item is included in nep-big, nep-ene, nep-for and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

Published in Energies 11.9 (2018) 2364, https://doi.org/10.3390/en11092364)

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Related works:
Journal Article: Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting (2018)
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