Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

Bartosz Uniejewski, Jakub Nowotarski and Rafał Weron

Energies, 2016, vol. 9, issue 8, 1-22

Abstract: In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an 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: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (74)

Downloads: (external link)
https://www.mdpi.com/1996-1073/9/8/621/pdf (application/pdf)
https://www.mdpi.com/1996-1073/9/8/621/ (text/html)

Related works:
Working Paper: Automated variable selection and shrinkage for day-ahead electricity price forecasting (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:9:y:2016:i:8:p:621-:d:75423

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().