A note on averaging day-ahead electricity price forecasts across calibration windows

Katarzyna Hubicka, Grzegorz Marcjasz and Rafał Weron

No HSC/18/03, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: We propose a novel concept in energy forecasting and show that averaging day-ahead electricity price forecasts of a predictive model across 28- to 728-day calibration windows yields better results than selecting only one 'optimal' window length. Even more significant accuracy gains can be achieved by averaging over a few, carefully selected windows.

Keywords: Electricity price forecasting; Combining forecasts; Calibration window; Autoregression; NARX neural network; Committee machine; Diebold-Mariano test (search for similar items in EconPapers)
JEL-codes: C14 C22 C45 C51 C53 Q47 (search for similar items in EconPapers)
Pages: 5 pages
Date: 2018-07-07
New Economics Papers: this item is included in nep-cmp, nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

Forthcoming in IEEE Transactions on Sustainable Energy (2018), https://doi.org/10.1109/TSTE.2018.2869557

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