New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model
Accurate wind power forecasting can help reduce disturbance to the grid in wind power integration. In this paper, a short-term power forecasting model is established by using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and nonlinear fitting characteristics of support vect...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7161445 |
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| _version_ | 1832551794475008000 |
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| author | Hong You Shixiong Bai Rui Wang Zhixiong Li Shuchen Xiang Feng Huang |
| author_facet | Hong You Shixiong Bai Rui Wang Zhixiong Li Shuchen Xiang Feng Huang |
| author_sort | Hong You |
| collection | DOAJ |
| description | Accurate wind power forecasting can help reduce disturbance to the grid in wind power integration. In this paper, a short-term power forecasting model is established by using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and nonlinear fitting characteristics of support vector machines (SVM) to accurately predict wind power. First, the wind power data are preprocessed and decomposed to 6 stable power components using CEEMDAN, thus reducing the impact of excessive forecasting errors of oscillatory points at peaks and valleys. Then, particle swarm optimization (PSO) based on improved empirical mode decomposition is designed to optimize the kernel function and penalty factor of the SVM. It establishes a new short-term power forecasting CEEMDAN-combined model. Finally, each stable component data is processed using the power forecasting model, and then, the results are combined to get the final power forecasting value. Analysis of test results and comparative studies show that the RMSE and MAPE of the new model are only one-third of that of the traditional SVM algorithm. The forecasting accuracy and speed meet the requirements for safe operation of wind farms. |
| format | Article |
| id | doaj-art-34ff1fbd9afa471690c9a70483027c60 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-34ff1fbd9afa471690c9a70483027c602025-02-03T06:00:26ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/7161445New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN ModelHong You0Shixiong Bai1Rui Wang2Zhixiong Li3Shuchen Xiang4Feng Huang5School of Electrical and Information EngineeringSchool of Electrical and Information EngineeringSchool of Electrical and Information EngineeringSchool of Electrical and Information EngineeringSchool of Electrical and Information EngineeringSchool of Electrical and Information EngineeringAccurate wind power forecasting can help reduce disturbance to the grid in wind power integration. In this paper, a short-term power forecasting model is established by using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and nonlinear fitting characteristics of support vector machines (SVM) to accurately predict wind power. First, the wind power data are preprocessed and decomposed to 6 stable power components using CEEMDAN, thus reducing the impact of excessive forecasting errors of oscillatory points at peaks and valleys. Then, particle swarm optimization (PSO) based on improved empirical mode decomposition is designed to optimize the kernel function and penalty factor of the SVM. It establishes a new short-term power forecasting CEEMDAN-combined model. Finally, each stable component data is processed using the power forecasting model, and then, the results are combined to get the final power forecasting value. Analysis of test results and comparative studies show that the RMSE and MAPE of the new model are only one-third of that of the traditional SVM algorithm. The forecasting accuracy and speed meet the requirements for safe operation of wind farms.http://dx.doi.org/10.1155/2022/7161445 |
| spellingShingle | Hong You Shixiong Bai Rui Wang Zhixiong Li Shuchen Xiang Feng Huang New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model Journal of Electrical and Computer Engineering |
| title | New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model |
| title_full | New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model |
| title_fullStr | New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model |
| title_full_unstemmed | New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model |
| title_short | New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model |
| title_sort | new pso svm short term wind power forecasting algorithm based on the ceemdan model |
| url | http://dx.doi.org/10.1155/2022/7161445 |
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