A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster
Abstract Short-term day-ahead photovoltaic power prediction is of great significance for power system dispatch plan formulation. In this work, to improve the accuracy of photovoltaic power prediction, a TCN-Wpsformer (temporal convolutional network-window probability sparse Transformer) day-ahead ph...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-95843-9 |
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| author | Tong Yang Minan Tang Hanting Li Hongjie Wang Chuntao Rao |
| author_facet | Tong Yang Minan Tang Hanting Li Hongjie Wang Chuntao Rao |
| author_sort | Tong Yang |
| collection | DOAJ |
| description | Abstract Short-term day-ahead photovoltaic power prediction is of great significance for power system dispatch plan formulation. In this work, to improve the accuracy of photovoltaic power prediction, a TCN-Wpsformer (temporal convolutional network-window probability sparse Transformer) day-ahead photovoltaic power prediction model based on combining data restoration and FCM (fuzzy C means) cluster is proposed. The time code of the dataset obtained after data restoration and FCM clustering was spliced with the location code. A temporal convolutional neural network is introduced to extract temporal segment features and incorporate a self-attention mechanism. The short-term photovoltaic power prediction is outputted by the window probability sparse Transformer model in multiple steps. Compared with the original Transformer model, the window probability sparse Transformer model uses the window probability sparse self-attention mechanism. It captures the long-term dependencies while filtering out the time segment features with relatively high importance for computation, which improves the prediction accuracy and reduces the computational cost. The computing time is reduced to 68.83% and R squared is improved by 5.3% compared to Transformer. The comparison is made through 11 models, and the R squared of this model is above 99% while different data volume and different power station data. It proves that the model stability and cross scene generalisation ability is well. Meanwhile, it can also provide more accurate confidence intervals on the basis of point prediction, which has certain application value. |
| format | Article |
| id | doaj-art-702c8eeafac14bfda8496ca69b94cc1b |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-702c8eeafac14bfda8496ca69b94cc1b2025-08-20T01:56:01ZengNature PortfolioScientific Reports2045-23222025-04-0115112210.1038/s41598-025-95843-9A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM clusterTong Yang0Minan Tang1Hanting Li2Hongjie Wang3Chuntao Rao4School of Automation and Electrical Engineering, Lanzhou Jiaotong UniversitySchool of Automation and Electrical Engineering, Lanzhou Jiaotong UniversitySchool of Automation and Electrical Engineering, Lanzhou Jiaotong UniversitySchool of Automation and Electrical Engineering, Lanzhou Jiaotong UniversitySchool of Automation and Electrical Engineering, Lanzhou Jiaotong UniversityAbstract Short-term day-ahead photovoltaic power prediction is of great significance for power system dispatch plan formulation. In this work, to improve the accuracy of photovoltaic power prediction, a TCN-Wpsformer (temporal convolutional network-window probability sparse Transformer) day-ahead photovoltaic power prediction model based on combining data restoration and FCM (fuzzy C means) cluster is proposed. The time code of the dataset obtained after data restoration and FCM clustering was spliced with the location code. A temporal convolutional neural network is introduced to extract temporal segment features and incorporate a self-attention mechanism. The short-term photovoltaic power prediction is outputted by the window probability sparse Transformer model in multiple steps. Compared with the original Transformer model, the window probability sparse Transformer model uses the window probability sparse self-attention mechanism. It captures the long-term dependencies while filtering out the time segment features with relatively high importance for computation, which improves the prediction accuracy and reduces the computational cost. The computing time is reduced to 68.83% and R squared is improved by 5.3% compared to Transformer. The comparison is made through 11 models, and the R squared of this model is above 99% while different data volume and different power station data. It proves that the model stability and cross scene generalisation ability is well. Meanwhile, it can also provide more accurate confidence intervals on the basis of point prediction, which has certain application value.https://doi.org/10.1038/s41598-025-95843-9Photovoltaic power predictionData restorationFCM similar day clusterTemporal convolutional neural networkTCN-Wpsformer modelInterval forecast |
| spellingShingle | Tong Yang Minan Tang Hanting Li Hongjie Wang Chuntao Rao A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster Scientific Reports Photovoltaic power prediction Data restoration FCM similar day cluster Temporal convolutional neural network TCN-Wpsformer model Interval forecast |
| title | A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster |
| title_full | A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster |
| title_fullStr | A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster |
| title_full_unstemmed | A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster |
| title_short | A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster |
| title_sort | novel pv power prediction method with tcn wpsformer model considering data repair and fcm cluster |
| topic | Photovoltaic power prediction Data restoration FCM similar day cluster Temporal convolutional neural network TCN-Wpsformer model Interval forecast |
| url | https://doi.org/10.1038/s41598-025-95843-9 |
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