Efficient forecasting of electricity spot prices with expert and LASSO models

Bartosz Uniejewski and Rafał Weron

No HSC/18/02, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models, generally better than obtained from other variable selection schemes. Conducting an empirical study involving three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model and a well chosen variance stabilizing transformation indeed leads to significant accuracy gains compared to the typically used EPF models.

Keywords: Electricity spot price; Day-ahead market; Long-term seasonal component; LASSO; Automated variable selection; Variance stabilizing transformation (search for similar items in EconPapers)
JEL-codes: C14 C22 C51 C53 Q47 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2018-06-29
New Economics Papers: this item is included in nep-ene, nep-ets, nep-for and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (40)

Published in Energies 11.8 (2018) 2039; https://doi.org/10.3390/en11082039

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Journal Article: Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models (2018)
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