Research on Wind Power Prediction Model Based on Random Forest and SVR
Wind power generation is random and easily affected by external factors. In order to construct an effective prediction model based on wind power generation, a wind power prediction model based on principal component analysis (PCA) noise reduction, feature selection based on random forest model and...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2024-04-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://publications.eai.eu/index.php/ew/article/view/5758 |
| _version_ | 1827284608543096832 |
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| author | Zehui Wang Dianwei Chi |
| author_facet | Zehui Wang Dianwei Chi |
| author_sort | Zehui Wang |
| collection | DOAJ |
| description |
Wind power generation is random and easily affected by external factors. In order to construct an effective prediction model based on wind power generation, a wind power prediction model based on principal component analysis (PCA) noise reduction, feature selection based on random forest model and support vector regression (SVR) algorithm is proposed. First, in the data preprocessing stage, PCA is used for sample data denoising; then the random forest model is used to calculate the importance evaluation value of each feature to optimize the selection of feature parameters; finally, The SVR algorithm is applied for training and prediction. Experiments show that the prediction effect of the model based on random forest and SVR is excellent, the root mean square error(RMSE) is 0.086, the average absolute percentage error(MAPE) is 23.47%, and the coefficient of determination(R2) is 0.991. Compared with the traditional SVR model, the root mean square error of the method proposed in this paper is reduced by 95.9%, and the prediction accuracy and the fit of the prediction curve are significantly improved.
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| first_indexed | 2024-04-24T09:59:15Z |
| format | Article |
| id | doaj.art-e7f0f94c4c584c23b51280008c7d4a73 |
| institution | Directory Open Access Journal |
| issn | 2032-944X |
| language | English |
| last_indexed | 2024-04-24T09:59:15Z |
| publishDate | 2024-04-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Energy Web |
| spelling | doaj.art-e7f0f94c4c584c23b51280008c7d4a732024-04-13T19:01:45ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2024-04-011110.4108/ew.5758Research on Wind Power Prediction Model Based on Random Forest and SVRZehui Wang0Dianwei Chi1Yantai University Yantai Institute of Technology Wind power generation is random and easily affected by external factors. In order to construct an effective prediction model based on wind power generation, a wind power prediction model based on principal component analysis (PCA) noise reduction, feature selection based on random forest model and support vector regression (SVR) algorithm is proposed. First, in the data preprocessing stage, PCA is used for sample data denoising; then the random forest model is used to calculate the importance evaluation value of each feature to optimize the selection of feature parameters; finally, The SVR algorithm is applied for training and prediction. Experiments show that the prediction effect of the model based on random forest and SVR is excellent, the root mean square error(RMSE) is 0.086, the average absolute percentage error(MAPE) is 23.47%, and the coefficient of determination(R2) is 0.991. Compared with the traditional SVR model, the root mean square error of the method proposed in this paper is reduced by 95.9%, and the prediction accuracy and the fit of the prediction curve are significantly improved. https://publications.eai.eu/index.php/ew/article/view/5758PCArandom forestSVRwind powerprediction |
| spellingShingle | Zehui Wang Dianwei Chi Research on Wind Power Prediction Model Based on Random Forest and SVR EAI Endorsed Transactions on Energy Web PCA random forest SVR wind power prediction |
| title | Research on Wind Power Prediction Model Based on Random Forest and SVR |
| title_full | Research on Wind Power Prediction Model Based on Random Forest and SVR |
| title_fullStr | Research on Wind Power Prediction Model Based on Random Forest and SVR |
| title_full_unstemmed | Research on Wind Power Prediction Model Based on Random Forest and SVR |
| title_short | Research on Wind Power Prediction Model Based on Random Forest and SVR |
| title_sort | research on wind power prediction model based on random forest and svr |
| topic | PCA random forest SVR wind power prediction |
| url | https://publications.eai.eu/index.php/ew/article/view/5758 |
| work_keys_str_mv | AT zehuiwang researchonwindpowerpredictionmodelbasedonrandomforestandsvr AT dianweichi researchonwindpowerpredictionmodelbasedonrandomforestandsvr |