Novel nonlinear wind power prediction based on improved iterative algorithm
To effectively improve the accuracy of wind power prediction and reduce the load on the power grid, a new nonlinear wind power prediction model based on an improved iterative learning algorithm was investigated. Firstly, the actual wind conditions are equated to a non-linear model. Using the concept...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2448626 |
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| author | Fu Zhen-yu Lin Gui-quan Tian Wei-da Pan Zhi-hao Zhang Wei-cong |
| author_facet | Fu Zhen-yu Lin Gui-quan Tian Wei-da Pan Zhi-hao Zhang Wei-cong |
| author_sort | Fu Zhen-yu |
| collection | DOAJ |
| description | To effectively improve the accuracy of wind power prediction and reduce the load on the power grid, a new nonlinear wind power prediction model based on an improved iterative learning algorithm was investigated. Firstly, the actual wind conditions are equated to a non-linear model. Using the concept of nonlinear decomposition, the nonlinear model is divided into many linear subdomain models while taking into account the nonlinear impacts of temperature, wind direction, altitude, and speed on the model. Next, using CRITIC weight analysis, the ideal weights are determined. Then, the linear sub-domain model is fitted into the full non-linear wind power prediction model equation by utilizing the least squares method. And the objective function of the iterative algorithm for iterative optimization search is derived from the prediction equations that were previously developed. The final enhanced iterative technique for nonlinear wind power prediction is produced by merging the iterative algorithm with the nonlinear decomposition. Finally, a comparative study of wind power prediction under different prediction models was carried out. The research results showed that the average absolute error of wind power prediction and the root mean square error were 4.5841% and 0.2301%, respectively. In particular, the prediction accuracy improved by 8.28%. |
| format | Article |
| id | doaj-art-39f691b9048b4f4abeee6b9bf81893d2 |
| institution | Kabale University |
| issn | 2164-2583 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-39f691b9048b4f4abeee6b9bf81893d22025-01-06T18:42:54ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832025-12-0113110.1080/21642583.2024.2448626Novel nonlinear wind power prediction based on improved iterative algorithmFu Zhen-yu0Lin Gui-quan1Tian Wei-da2Pan Zhi-hao3Zhang Wei-cong4Zhanjiang Power Supply Bureau of Guangdong Power Grid Co., LTD, Zhanjiang, People’s Republic of ChinaZhanjiang Power Supply Bureau of Guangdong Power Grid Co., LTD, Zhanjiang, People’s Republic of ChinaZhanjiang Power Supply Bureau of Guangdong Power Grid Co., LTD, Zhanjiang, People’s Republic of ChinaZhanjiang Power Supply Bureau of Guangdong Power Grid Co., LTD, Zhanjiang, People’s Republic of ChinaFaculty of Automation, Huaiyin Institute of Technology, Huai’an, People’s Republic of ChinaTo effectively improve the accuracy of wind power prediction and reduce the load on the power grid, a new nonlinear wind power prediction model based on an improved iterative learning algorithm was investigated. Firstly, the actual wind conditions are equated to a non-linear model. Using the concept of nonlinear decomposition, the nonlinear model is divided into many linear subdomain models while taking into account the nonlinear impacts of temperature, wind direction, altitude, and speed on the model. Next, using CRITIC weight analysis, the ideal weights are determined. Then, the linear sub-domain model is fitted into the full non-linear wind power prediction model equation by utilizing the least squares method. And the objective function of the iterative algorithm for iterative optimization search is derived from the prediction equations that were previously developed. The final enhanced iterative technique for nonlinear wind power prediction is produced by merging the iterative algorithm with the nonlinear decomposition. Finally, a comparative study of wind power prediction under different prediction models was carried out. The research results showed that the average absolute error of wind power prediction and the root mean square error were 4.5841% and 0.2301%, respectively. In particular, the prediction accuracy improved by 8.28%.https://www.tandfonline.com/doi/10.1080/21642583.2024.2448626Iterative algorithmwind power forecastingweighting analysislinear subdomain modelnonlinear predictive model |
| spellingShingle | Fu Zhen-yu Lin Gui-quan Tian Wei-da Pan Zhi-hao Zhang Wei-cong Novel nonlinear wind power prediction based on improved iterative algorithm Systems Science & Control Engineering Iterative algorithm wind power forecasting weighting analysis linear subdomain model nonlinear predictive model |
| title | Novel nonlinear wind power prediction based on improved iterative algorithm |
| title_full | Novel nonlinear wind power prediction based on improved iterative algorithm |
| title_fullStr | Novel nonlinear wind power prediction based on improved iterative algorithm |
| title_full_unstemmed | Novel nonlinear wind power prediction based on improved iterative algorithm |
| title_short | Novel nonlinear wind power prediction based on improved iterative algorithm |
| title_sort | novel nonlinear wind power prediction based on improved iterative algorithm |
| topic | Iterative algorithm wind power forecasting weighting analysis linear subdomain model nonlinear predictive model |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2448626 |
| work_keys_str_mv | AT fuzhenyu novelnonlinearwindpowerpredictionbasedonimprovediterativealgorithm AT linguiquan novelnonlinearwindpowerpredictionbasedonimprovediterativealgorithm AT tianweida novelnonlinearwindpowerpredictionbasedonimprovediterativealgorithm AT panzhihao novelnonlinearwindpowerpredictionbasedonimprovediterativealgorithm AT zhangweicong novelnonlinearwindpowerpredictionbasedonimprovediterativealgorithm |