EconPapers    
Economics at your fingertips  
 

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Arkadiusz Jędrzejewski (), Grzegorz Marcjasz () and Rafał Weron ()
Additional contact information
Arkadiusz Jędrzejewski: Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland

Energies, 2021, vol. 14, issue 11, 1-17

Abstract: Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.

Keywords: electricity price forecasting; day-ahead market; LASSO; long-term seasonal component; variance stabilizing transformation; forecast averaging (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/11/3249/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/11/3249/ (text/html)

Related works:
Working Paper: Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO (2021) Downloads
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:14:y:2021:i:11:p:3249-:d:567421

Access Statistics for this article

Energies is currently edited by Prof. Dr. Enrico Sciubba

More articles in Energies from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().

 
Page updated 2021-10-16
Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3249-:d:567421