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Automated variable selection and shrinkage for day-ahead electricity price forecasting

Bartosz Uniejewski, Jakub Nowotarski and Rafał Weron

No HSC/16/06, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Technology

Abstract: In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an extensive empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets) we show that using the latter two classes can bring significant accuracy gains compared to commonly used EPF models. In particular, one of the elastic nets - a class that has not been considered in EPF before - stands out as the best performing model overall.

Keywords: Electricity price forecasting; Day-ahead market; Autoregression; Variable selection; Stepwise regression; Ridge regression; Lasso; Elastic net (search for similar items in EconPapers)
JEL-codes: C14 C22 C51 C53 Q47 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2016-07-05
New Economics Papers: this item is included in nep-ene, nep-for, nep-ore and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (73)

Published in Energies 2016, vol. 9(8), 621; doi:10.3390/en9080621

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Journal Article: Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting (2016) Downloads
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