Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction
A short-term photovoltaic power combination prediction method based on similar day selection and data reconstruction is proposed to address the strong randomness of photovoltaic power. Firstly, clustering analysis of photovoltaic power is performed using the kernel fuzzy C-means algorithm, and the m...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2024-12-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202406005 |
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| author | Qingbin CHEN Genghuang YANG Liqing GENG Juan SU Jingsheng SUN |
| author_facet | Qingbin CHEN Genghuang YANG Liqing GENG Juan SU Jingsheng SUN |
| author_sort | Qingbin CHEN |
| collection | DOAJ |
| description | A short-term photovoltaic power combination prediction method based on similar day selection and data reconstruction is proposed to address the strong randomness of photovoltaic power. Firstly, clustering analysis of photovoltaic power is performed using the kernel fuzzy C-means algorithm, and the main influencing features are extracted through the maximum information coefficient. Secondly, the cooperative game theory is used to calculate the comprehensive correlation coefficient between the predicted days and the historical days, and the historical days with strong correlation are selected to construct the training set. Then, the variational mode decomposition method is used to decompose the photovoltaic power into several subsequences, and the permutation entropy is calculated and reconstructed into trend, low-frequency, and high-frequency terms. Finally, the long short-term memory neural networks are used to predict the trend and low-frequency items, while the convolutional neural network-bidirectional long short-term memory-attention models are used to predict the high-frequency items. The final prediction result is obtained by overlaying the results. Through practical examples, it has been verified that under different weather conditions, the overall prediction error of the model is the smallest, which can effectively improve the prediction accuracy. |
| format | Article |
| id | doaj-art-2e42f894704c4e03978ecae2f722464d |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-2e42f894704c4e03978ecae2f722464d2025-08-20T02:47:32ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492024-12-015712718110.11930/j.issn.1004-9649.202406005zgdl-57-12-chenqingbinShort Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data ReconstructionQingbin CHEN0Genghuang YANG1Liqing GENG2Juan SU3Jingsheng SUN4School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaSchool of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaSchool of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaState Grid Tianjin Electric Power Company Comprehensive Service Center, Tianjin 300010, ChinaA short-term photovoltaic power combination prediction method based on similar day selection and data reconstruction is proposed to address the strong randomness of photovoltaic power. Firstly, clustering analysis of photovoltaic power is performed using the kernel fuzzy C-means algorithm, and the main influencing features are extracted through the maximum information coefficient. Secondly, the cooperative game theory is used to calculate the comprehensive correlation coefficient between the predicted days and the historical days, and the historical days with strong correlation are selected to construct the training set. Then, the variational mode decomposition method is used to decompose the photovoltaic power into several subsequences, and the permutation entropy is calculated and reconstructed into trend, low-frequency, and high-frequency terms. Finally, the long short-term memory neural networks are used to predict the trend and low-frequency items, while the convolutional neural network-bidirectional long short-term memory-attention models are used to predict the high-frequency items. The final prediction result is obtained by overlaying the results. Through practical examples, it has been verified that under different weather conditions, the overall prediction error of the model is the smallest, which can effectively improve the prediction accuracy.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202406005photovoltaic powersimilar daysvariational mode decompositionbidirectional long short term memory neural networkcombination prediction |
| spellingShingle | Qingbin CHEN Genghuang YANG Liqing GENG Juan SU Jingsheng SUN Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction Zhongguo dianli photovoltaic power similar days variational mode decomposition bidirectional long short term memory neural network combination prediction |
| title | Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction |
| title_full | Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction |
| title_fullStr | Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction |
| title_full_unstemmed | Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction |
| title_short | Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction |
| title_sort | short term photovoltaic power combination prediction method based on similar day selection and data reconstruction |
| topic | photovoltaic power similar days variational mode decomposition bidirectional long short term memory neural network combination prediction |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202406005 |
| work_keys_str_mv | AT qingbinchen shorttermphotovoltaicpowercombinationpredictionmethodbasedonsimilardayselectionanddatareconstruction AT genghuangyang shorttermphotovoltaicpowercombinationpredictionmethodbasedonsimilardayselectionanddatareconstruction AT liqinggeng shorttermphotovoltaicpowercombinationpredictionmethodbasedonsimilardayselectionanddatareconstruction AT juansu shorttermphotovoltaicpowercombinationpredictionmethodbasedonsimilardayselectionanddatareconstruction AT jingshengsun shorttermphotovoltaicpowercombinationpredictionmethodbasedonsimilardayselectionanddatareconstruction |