A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets

Ciaran O’Connor (), Mohamed Bahloul, Steven Prestwich () and Andrea Visentin
Additional contact information
Ciaran O’Connor: SFI CRT in Artificial Intelligence, School of Computer Science & IT, University College Cork, T12 YN60 Cork, Ireland
Mohamed Bahloul: Water & Energy Transition Unit, Vlaamse Instelling voor Technologisch Onderzoek, 2400 Mol, Belgium
Steven Prestwich: Insight Centre for Data Analytics, School of Computer Science & IT, University College Cork, T12 YN60 Cork, Ireland
Andrea Visentin: Insight Centre for Data Analytics, School of Computer Science & IT, University College Cork, T12 YN60 Cork, Ireland

Energies, 2025, vol. 18, issue 12, 1-40

Abstract: Electricity price forecasting plays a fundamental role in ensuring efficient market operation and informed decision making. With the growing integration of renewable energy, prices have become more volatile and difficult to predict, increasing the necessity of accurate forecasting in bidding, scheduling, and risk management. This paper provides a comprehensive review of point forecasting models for electricity markets, covering classical statistical approaches both with and without exogenous inputs, and modern machine learning and deep learning techniques, including ensemble methods and hybrid architectures. Unlike standard reviews focused solely on the day-ahead market, we assess model performance across day-ahead, intra-day, and balancing markets, with each posing unique challenges due to differences in time resolution, data availability, and market structure. Through this market-specific lens, the paper merges insights from a broad set of studies; identifies persistent challenges, such as data quality, model interpretability, and generalisability; and outlines promising directions for future research. Our findings highlight the strong performance of hybrid and ensemble models in the day-ahead market, the dominance of recurrent neural networks in the intra-day market, and the relative effectiveness of simpler statistical models such as LEAR in the balancing market, where volatility and data sparsity remain critical challenges.

Keywords: electricity price forecasting; day-ahead market; intra-day market; balancing market; machine learning; deep learning; hybrid models (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/12/3097/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/12/3097/ (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:jeners:v:18:y:2025:i:12:p:3097-:d:1677361

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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