A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets
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, schedu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/12/3097 |
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| author | Ciaran O’Connor Mohamed Bahloul Steven Prestwich Andrea Visentin |
| author_facet | Ciaran O’Connor Mohamed Bahloul Steven Prestwich Andrea Visentin |
| author_sort | Ciaran O’Connor |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e5b179709131417b9dbe91d47b26c75c |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e5b179709131417b9dbe91d47b26c75c2025-08-20T03:27:10ZengMDPI AGEnergies1996-10732025-06-011812309710.3390/en18123097A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing MarketsCiaran O’Connor0Mohamed Bahloul1Steven Prestwich2Andrea Visentin3SFI CRT in Artificial Intelligence, School of Computer Science & IT, University College Cork, T12 YN60 Cork, IrelandWater & Energy Transition Unit, Vlaamse Instelling voor Technologisch Onderzoek, 2400 Mol, BelgiumInsight Centre for Data Analytics, School of Computer Science & IT, University College Cork, T12 YN60 Cork, IrelandInsight Centre for Data Analytics, School of Computer Science & IT, University College Cork, T12 YN60 Cork, IrelandElectricity 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.https://www.mdpi.com/1996-1073/18/12/3097electricity price forecastingday-ahead marketintra-day marketbalancing marketmachine learningdeep learning |
| spellingShingle | Ciaran O’Connor Mohamed Bahloul Steven Prestwich Andrea Visentin A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets Energies electricity price forecasting day-ahead market intra-day market balancing market machine learning deep learning |
| title | A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets |
| title_full | A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets |
| title_fullStr | A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets |
| title_full_unstemmed | A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets |
| title_short | A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets |
| title_sort | review of electricity price forecasting models in the day ahead intra day and balancing markets |
| topic | electricity price forecasting day-ahead market intra-day market balancing market machine learning deep learning |
| url | https://www.mdpi.com/1996-1073/18/12/3097 |
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