Issue: 2024/Vol.34/No.3, Pages 267-286
REGULARIZATION FOR ELECTRICITY PRICE FORECASTING
Bartosz Uniejewski
Cite as: B. Uniejewski. Regularization for electricity price forecasting. Operations Research and Decisions 2024: 34(3), 267-286. DOI 10.37190/ord2403014
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.
Keywords: electricity price forecasting, regularization, power market, convex regularization, LQ regularization, elastic net
Received: 25 September 2023 Accepted: 14 July 2024
Published online: 17 October 2024