Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods
In the context of developing a unified national electricity market， the development of a spot market helps promote the sharing and optimal allocation of electricity resources on a larger scale. As important decision-making information for market participants， spot electricity prices are crucial for...
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Editorial Department of Electric Power Construction
2025-02-01
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| Series: | Dianli jianshe |
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| Online Access: | https://www.cepc.com.cn/fileup/1000-7229/PDF/1738997592061-910732370.pdf |
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| author | JIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan |
| author_facet | JIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan |
| author_sort | JIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan |
| collection | DOAJ |
| description | In the context of developing a unified national electricity market， the development of a spot market helps promote the sharing and optimal allocation of electricity resources on a larger scale. As important decision-making information for market participants， spot electricity prices are crucial for auxiliary decision-making in the spot market， market operation monitoring， and risk management. The rapid development of machine learning methods provided a feasible approach for electricity price prediction. This study first analyzed the characteristics of spot electricity prices and their influence on the unified national electricity market. The types of prediction models and challenges faced by spot electricity price prediction can be elaborated based on existing research on electricity price prediction mechanisms. In addition， based on the characteristics of data labeling， feature extraction and data flow control， the research status of various machine learning prediction models was summarized， and the characteristics and applicability of different prediction models were analyzed. This study then analyzed the evaluation criteria for spot electricity price prediction models based on machine learning， and summarized the model hyperparameter training requirements and the practical application of relevant prediction methods. Finally， in view of the challenges of machine learning methods in electricity price prediction research， this study outlined future research directions to provide constructive references for the development of the spot market under the construction of a unified national electricity market. |
| format | Article |
| id | doaj-art-fc7661314a1a4ea9bb4c667f96b74ceb |
| institution | Kabale University |
| issn | 1000-7229 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Editorial Department of Electric Power Construction |
| record_format | Article |
| series | Dianli jianshe |
| spelling | doaj-art-fc7661314a1a4ea9bb4c667f96b74ceb2025-02-10T09:54:54ZzhoEditorial Department of Electric Power ConstructionDianli jianshe1000-72292025-02-0146216017910.12204/j.issn.1000-7229.2025.02.014Review of Spot Electricity Price Prediction Studies Based on Machine Learning MethodsJIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan01. Beijing Key Laboratory of New Energy and Low-Carbon Development， Beijing 102206， China;2. School of Economics & Management， North China Electric Power University， Beijing 102206， China;3. China Electric Power Research Institute， Beijing 100192， ChinaIn the context of developing a unified national electricity market， the development of a spot market helps promote the sharing and optimal allocation of electricity resources on a larger scale. As important decision-making information for market participants， spot electricity prices are crucial for auxiliary decision-making in the spot market， market operation monitoring， and risk management. The rapid development of machine learning methods provided a feasible approach for electricity price prediction. This study first analyzed the characteristics of spot electricity prices and their influence on the unified national electricity market. The types of prediction models and challenges faced by spot electricity price prediction can be elaborated based on existing research on electricity price prediction mechanisms. In addition， based on the characteristics of data labeling， feature extraction and data flow control， the research status of various machine learning prediction models was summarized， and the characteristics and applicability of different prediction models were analyzed. This study then analyzed the evaluation criteria for spot electricity price prediction models based on machine learning， and summarized the model hyperparameter training requirements and the practical application of relevant prediction methods. Finally， in view of the challenges of machine learning methods in electricity price prediction research， this study outlined future research directions to provide constructive references for the development of the spot market under the construction of a unified national electricity market.https://www.cepc.com.cn/fileup/1000-7229/PDF/1738997592061-910732370.pdfnational unified electricity market|spot market|electricity price forecasting|machine learning methods |
| spellingShingle | JIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods Dianli jianshe national unified electricity market|spot market|electricity price forecasting|machine learning methods |
| title | Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods |
| title_full | Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods |
| title_fullStr | Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods |
| title_full_unstemmed | Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods |
| title_short | Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods |
| title_sort | review of spot electricity price prediction studies based on machine learning methods |
| topic | national unified electricity market|spot market|electricity price forecasting|machine learning methods |
| url | https://www.cepc.com.cn/fileup/1000-7229/PDF/1738997592061-910732370.pdf |
| work_keys_str_mv | AT jiahepingguoyuchenmaqianxinyangzhenglinzhengyaxianzengdanliudunnan reviewofspotelectricitypricepredictionstudiesbasedonmachinelearningmethods |