Short-term Power Prediction of Photovoltaic Power Generation Based on LSTM and Error Correction
In order to improve the stability of photovoltaic power grid connection and make full use of error information to correct the model prediction results, this paper proposes a short-term photovoltaic power prediction model based on long short-term memory (LSTM) and error correction. First, the data is...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2025-04-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2420 |
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| author | ZHU Tao LI Junwei ZHU Yuanfu YE Zhiming TANG Yi |
| author_facet | ZHU Tao LI Junwei ZHU Yuanfu YE Zhiming TANG Yi |
| author_sort | ZHU Tao |
| collection | DOAJ |
| description | In order to improve the stability of photovoltaic power grid connection and make full use of error information to correct the model prediction results, this paper proposes a short-term photovoltaic power prediction model based on long short-term memory (LSTM) and error correction. First, the data is preliminarily predicted by LSTM to generate an error sequence, and then the error sequence is decomposed into submodels of different frequencies by empirical mode decomposition (EMD). Similarity measurement is conducted according to Hausdorff distance ( HD), and each modal component is assigned weights, and then LSTM optimized by Sparrow Search Algorithm ( SSA) is used to predict error modal components. The weighted prediction error is combined with the predicted value to achieve error correction. Through experiments, it has been proven that the model proposed in this article outperforms traditional LSTM models, BP models, and SVM models in evaluation indicators such as root mean square error (RMSE) and mean absolute percentage error (MAPE), verifying the effectiveness of the combined model. |
| format | Article |
| id | doaj-art-34aa3ee8e68946bab5ca4b8a6cff5f2b |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2025-04-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-34aa3ee8e68946bab5ca4b8a6cff5f2b2025-08-20T03:29:09ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832025-04-01300212213010.15938/j.jhust.2025.02.013Short-term Power Prediction of Photovoltaic Power Generation Based on LSTM and Error CorrectionZHU Tao0LI Junwei1ZHU Yuanfu2YE Zhiming3TANG Yi4Kunming Power Supply Bureau of Yunnan Power Grid Co. , Ltd. , Kunming 650011, ChinaKunming Power Supply Bureau of Yunnan Power Grid Co. , Ltd. , Kunming 650011, ChinaKunming Power Supply Bureau of Yunnan Power Grid Co. , Ltd. , Kunming 650011, ChinaKunming Power Supply Bureau of Yunnan Power Grid Co. , Ltd. , Kunming 650011, ChinaSchool of Electrical Engineering, Southeast University, Nanjing 210092, China; Nanjing Dongbo Smart Energy Research Institute Co. , Ltd. , Nanjing 210004, ChinaIn order to improve the stability of photovoltaic power grid connection and make full use of error information to correct the model prediction results, this paper proposes a short-term photovoltaic power prediction model based on long short-term memory (LSTM) and error correction. First, the data is preliminarily predicted by LSTM to generate an error sequence, and then the error sequence is decomposed into submodels of different frequencies by empirical mode decomposition (EMD). Similarity measurement is conducted according to Hausdorff distance ( HD), and each modal component is assigned weights, and then LSTM optimized by Sparrow Search Algorithm ( SSA) is used to predict error modal components. The weighted prediction error is combined with the predicted value to achieve error correction. Through experiments, it has been proven that the model proposed in this article outperforms traditional LSTM models, BP models, and SVM models in evaluation indicators such as root mean square error (RMSE) and mean absolute percentage error (MAPE), verifying the effectiveness of the combined model.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2420photovoltaic power generationerror correctionoptimization algorithmempirical mode decompositionpower prediction |
| spellingShingle | ZHU Tao LI Junwei ZHU Yuanfu YE Zhiming TANG Yi Short-term Power Prediction of Photovoltaic Power Generation Based on LSTM and Error Correction Journal of Harbin University of Science and Technology photovoltaic power generation error correction optimization algorithm empirical mode decomposition power prediction |
| title | Short-term Power Prediction of Photovoltaic Power Generation Based on LSTM and Error Correction |
| title_full | Short-term Power Prediction of Photovoltaic Power Generation Based on LSTM and Error Correction |
| title_fullStr | Short-term Power Prediction of Photovoltaic Power Generation Based on LSTM and Error Correction |
| title_full_unstemmed | Short-term Power Prediction of Photovoltaic Power Generation Based on LSTM and Error Correction |
| title_short | Short-term Power Prediction of Photovoltaic Power Generation Based on LSTM and Error Correction |
| title_sort | short term power prediction of photovoltaic power generation based on lstm and error correction |
| topic | photovoltaic power generation error correction optimization algorithm empirical mode decomposition power prediction |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2420 |
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