Regularization for electricity price forecasting

Bartosz Uniejewski

Papers from arXiv.org

Abstract: The most commonly used form of regularization typically involves defining the penalty function as a L1 or L2 norm. However, numerous alternative approaches remain untested in practical applications. In this study, we apply ten different penalty functions to predict electricity prices and evaluate their performance under two different model structures and in two distinct electricity markets. The study reveals that LQ and elastic net consistently produce more accurate forecasts compared to other regularization types. In particular, they were the only types of penalty functions that consistently produced more accurate forecasts than the most commonly used LASSO. Furthermore, the results suggest that cross-validation outperforms Bayesian information criteria for parameter optimization, and performs as well as models with ex-post parameter selection.

Date: 2024-04
New Economics Papers: this item is included in nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Operations Research and Decisions 2024, 34(3), 267-286

Downloads: (external link)
http://arxiv.org/pdf/2404.03968 Latest version (application/pdf)

Related works:
Journal Article: Regularization for electricity price forecasting (2024)
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:arx:papers:2404.03968

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().