Electricity Spot Price Forecast by Modelling Supply and Demand Curve

Miguel Pinhão, Miguel Fonseca and Ricardo Covas
Additional contact information
Miguel Pinhão: NOVA School of Science and Technology, Department of Mathematics, NOVA Math, 2829-516 Caparica, Portugal
Miguel Fonseca: NOVA School of Science and Technology, Department of Mathematics, NOVA Math, 2829-516 Caparica, Portugal
Ricardo Covas: EDP—Energias de Portugal, 1249-300 Lisboa, Portugal

Mathematics, 2022, vol. 10, issue 12, 1-20

Abstract: Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Most methods forecast the electricity price itself; this paper gives a new perspective to the field by trying to forecast the dynamics behind the electricity price: the supply and demand curves originating from the auction. Given the complexity of the data involved which include many block bids/offers per hour, we propose a technique for market curve modeling and forecasting that incorporates multiple seasonal effects and known market variables, such as wind generation or load. It is shown that this model outperforms the benchmarked ones and increases the performance of ensemble models, highlighting the importance of the use of market bids in electricity price forecasting.

Keywords: electricity price forecast; electricity; market curves; electricity price; vector auto regression; time-series (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/12/2012/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/12/2012/ (text/html)

Related works:
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:jmathe:v:10:y:2022:i:12:p:2012-:d:836488

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

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