Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model
A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to photovoltaic (PV) power generation are f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/403 |
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| author | Shengli Wang Xiaolong Guo Tianle Sun Lihui Xu Jinfeng Zhu Zhicai Li Jinjiang Zhang |
| author_facet | Shengli Wang Xiaolong Guo Tianle Sun Lihui Xu Jinfeng Zhu Zhicai Li Jinjiang Zhang |
| author_sort | Shengli Wang |
| collection | DOAJ |
| description | A short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to photovoltaic (PV) power generation are filtered using the Spearman correlation coefficient. Historical data are then clustered into three categories—sunny, cloudy, and rainy days—using the K-means algorithm. Next, the original PV power data are decomposed through VMD. A DHKELM-based combined prediction model is developed for each component of the decomposition, tailored to different weather types. The model’s hyperparameters are optimized using the IDBO. The final power forecast is determined by combining the outcomes of each individual component. Validation is performed using actual data from a PV power plant in Australia and a PV power station in Kashgar, China demonstrates. Numerical evaluation results show that the proposed method improves the Mean Absolute Error (MAE) by 3.84% and the Root-Mean-Squared Error (RMSE) by 3.38%, confirming its accuracy. |
| format | Article |
| id | doaj-art-b5979c9ef5b749f5a55e27437832d74d |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-b5979c9ef5b749f5a55e27437832d74d2025-01-24T13:31:22ZengMDPI AGEnergies1996-10732025-01-0118240310.3390/en18020403Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM ModelShengli Wang0Xiaolong Guo1Tianle Sun2Lihui Xu3Jinfeng Zhu4Zhicai Li5Jinjiang Zhang6State Grid Kashgar Power Supply Company, Kashgar 844000, ChinaState Grid Kashgar Power Supply Company, Kashgar 844000, ChinaSchool of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaState Grid Kashgar Power Supply Company, Kashgar 844000, ChinaState Grid Kashgar Power Supply Company, Kashgar 844000, ChinaState Grid Kashgar Power Supply Company, Kashgar 844000, ChinaSchool of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaA short-term photovoltaic power forecasting method is proposed, integrating variational mode decomposition (VMD), an improved dung beetle algorithm (IDBO), and a deep hybrid kernel extreme learning machine (DHKELM). First, the weather factors less relevant to photovoltaic (PV) power generation are filtered using the Spearman correlation coefficient. Historical data are then clustered into three categories—sunny, cloudy, and rainy days—using the K-means algorithm. Next, the original PV power data are decomposed through VMD. A DHKELM-based combined prediction model is developed for each component of the decomposition, tailored to different weather types. The model’s hyperparameters are optimized using the IDBO. The final power forecast is determined by combining the outcomes of each individual component. Validation is performed using actual data from a PV power plant in Australia and a PV power station in Kashgar, China demonstrates. Numerical evaluation results show that the proposed method improves the Mean Absolute Error (MAE) by 3.84% and the Root-Mean-Squared Error (RMSE) by 3.38%, confirming its accuracy.https://www.mdpi.com/1996-1073/18/2/403photovoltaic power forecastK-meansimproved dung beetle optimizervariational mode decompositiondeep hybrid learningkernel extremum learning machine |
| spellingShingle | Shengli Wang Xiaolong Guo Tianle Sun Lihui Xu Jinfeng Zhu Zhicai Li Jinjiang Zhang Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model Energies photovoltaic power forecast K-means improved dung beetle optimizer variational mode decomposition deep hybrid learning kernel extremum learning machine |
| title | Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model |
| title_full | Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model |
| title_fullStr | Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model |
| title_full_unstemmed | Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model |
| title_short | Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model |
| title_sort | short term photovoltaic power forecasting based on the vmd idbo dhkelm model |
| topic | photovoltaic power forecast K-means improved dung beetle optimizer variational mode decomposition deep hybrid learning kernel extremum learning machine |
| url | https://www.mdpi.com/1996-1073/18/2/403 |
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