Research on Power Price Forecasting Based on PSO-XGBoost
With the reform of the power system, the prediction of power market pricing has become one of the key problems that needs to be solved in time. Power price prediction plays an important role in maximizing the profits of the participants in the power market and making full use of power energy. In ord...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/22/3763 |
| _version_ | 1797465483743592448 |
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| author | Kehe Wu Yanyu Chai Xiaoliang Zhang Xun Zhao |
| author_facet | Kehe Wu Yanyu Chai Xiaoliang Zhang Xun Zhao |
| author_sort | Kehe Wu |
| collection | DOAJ |
| description | With the reform of the power system, the prediction of power market pricing has become one of the key problems that needs to be solved in time. Power price prediction plays an important role in maximizing the profits of the participants in the power market and making full use of power energy. In order to improve the prediction accuracy of the power price, this paper proposes a power price prediction method based on PSO optimization of the XGBoost model, which optimizes eight main parameters of the XGBoost model through particle swarm optimization to improve the prediction accuracy of the XGBoost model. Using the electricity price data of Australia from January to December 2019, the proposed model is compared with the XGBoost model. The experimental results show that PSO can effectively improve the performance of the model. In addition, the prediction results of PSO-XGBoost are compared with those of SVM, LSTM, ARIMA, RW and XGBoost, and the average relative error and root mean square error of different power price prediction models are calculated. The experimental results show that the prediction accuracy of the PSO-XGBoost model is higher and more in line with the actual trend of power price change. |
| first_indexed | 2024-03-09T18:22:07Z |
| format | Article |
| id | doaj.art-06873117600f44269e5d715d2835531a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T18:22:07Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-06873117600f44269e5d715d2835531a2023-11-24T08:10:08ZengMDPI AGElectronics2079-92922022-11-011122376310.3390/electronics11223763Research on Power Price Forecasting Based on PSO-XGBoostKehe Wu0Yanyu Chai1Xiaoliang Zhang2Xun Zhao3School of Control and Computer Engineering, North China Electric Power University, Beijing 100096, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing 100096, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing 100096, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing 100096, ChinaWith the reform of the power system, the prediction of power market pricing has become one of the key problems that needs to be solved in time. Power price prediction plays an important role in maximizing the profits of the participants in the power market and making full use of power energy. In order to improve the prediction accuracy of the power price, this paper proposes a power price prediction method based on PSO optimization of the XGBoost model, which optimizes eight main parameters of the XGBoost model through particle swarm optimization to improve the prediction accuracy of the XGBoost model. Using the electricity price data of Australia from January to December 2019, the proposed model is compared with the XGBoost model. The experimental results show that PSO can effectively improve the performance of the model. In addition, the prediction results of PSO-XGBoost are compared with those of SVM, LSTM, ARIMA, RW and XGBoost, and the average relative error and root mean square error of different power price prediction models are calculated. The experimental results show that the prediction accuracy of the PSO-XGBoost model is higher and more in line with the actual trend of power price change.https://www.mdpi.com/2079-9292/11/22/3763particle swarm optimizationXGBoostelectricity marketprice forecast |
| spellingShingle | Kehe Wu Yanyu Chai Xiaoliang Zhang Xun Zhao Research on Power Price Forecasting Based on PSO-XGBoost Electronics particle swarm optimization XGBoost electricity market price forecast |
| title | Research on Power Price Forecasting Based on PSO-XGBoost |
| title_full | Research on Power Price Forecasting Based on PSO-XGBoost |
| title_fullStr | Research on Power Price Forecasting Based on PSO-XGBoost |
| title_full_unstemmed | Research on Power Price Forecasting Based on PSO-XGBoost |
| title_short | Research on Power Price Forecasting Based on PSO-XGBoost |
| title_sort | research on power price forecasting based on pso xgboost |
| topic | particle swarm optimization XGBoost electricity market price forecast |
| url | https://www.mdpi.com/2079-9292/11/22/3763 |
| work_keys_str_mv | AT kehewu researchonpowerpriceforecastingbasedonpsoxgboost AT yanyuchai researchonpowerpriceforecastingbasedonpsoxgboost AT xiaoliangzhang researchonpowerpriceforecastingbasedonpsoxgboost AT xunzhao researchonpowerpriceforecastingbasedonpsoxgboost |