Regularization for Electricity Price Forecasting
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 th...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wrocław University of Science and Technology
2024-01-01
|
| Series: | Operations Research and Decisions |
| Online Access: | https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no3_14.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. (original abstract) |
|---|---|
| ISSN: | 2081-8858 2391-6060 |