Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba
As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, th...
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024012775 |
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| author | Chenhao Cai Leyao Zhang Jianguo Zhou Luming Zhou |
| author_facet | Chenhao Cai Leyao Zhang Jianguo Zhou Luming Zhou |
| author_sort | Chenhao Cai |
| collection | DOAJ |
| description | As the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems. |
| format | Article |
| id | doaj-art-e5ceeca8d6d3483f8285a09d46fbbba5 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-e5ceeca8d6d3483f8285a09d46fbbba52024-12-19T10:57:50ZengElsevierResults in Engineering2590-12302024-12-0124103022Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision MambaChenhao Cai0Leyao Zhang1Jianguo Zhou2Luming Zhou3Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding, 071000, China; Corresponding author.National Engineering Research Center for E-Learning, Central China Normal University, Luoyu Road, Wuhan, 430079, Hubei, ChinaDepartment of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding, 071000, ChinaDepartment of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding, 071000, ChinaAs the global demand for sustainable energy sources continues to grow, accurate prediction of photovoltaic power generation is crucial for optimizing the utilization of solar resources and enhancing the efficiency of photovoltaic systems. To improve the accuracy of photovoltaic power forecasting, this paper proposes a novel hybrid predictive model that integrates Optimized Variational Mode Decomposition (VMD), Vision Mamba (Vim) for extracting features from sky images, and advanced mechanisms like Patch Embedding and Variate-wise Cross-Attention. Initially, the proposed model employs SAO-optimized VMD to decompose the photovoltaic power series into high, medium, and low-frequency components. Subsequently, these components are patched to serve as input for the subsequent layers. In the third step, exogenous variables, including meteorological and image data, are introduced and processed through Variate Embedding combined with cross-attention mechanisms to capture the intricate interactions between these variables. Finally, by integrating the outputs from all processing steps through normalization and feed-forward layers, the final predictive results are produced. Experimental evaluations across different seasons demonstrate significant enhancements in forecasting accuracy, with the model achieving Root Mean Square Error (RMSE) values of 0.3587 in spring, 0.4376 in summer, 0.3544 in autumn, and 0.3493 in winter. Similarly, Mean Absolute Error (MAE) and Mean Squared Error (MSE) across these seasons underscore the model's effectiveness. This model offers new technical means for photovoltaic power forecasting and provides valuable decision support for the optimization and management of photovoltaic power systems.http://www.sciencedirect.com/science/article/pii/S2590123024012775Photovoltaic power forecastingVision MambaVariational mode decompositionSnow ablation optimization |
| spellingShingle | Chenhao Cai Leyao Zhang Jianguo Zhou Luming Zhou Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba Results in Engineering Photovoltaic power forecasting Vision Mamba Variational mode decomposition Snow ablation optimization |
| title | Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba |
| title_full | Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba |
| title_fullStr | Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba |
| title_full_unstemmed | Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba |
| title_short | Sky images based photovoltaic power forecasting: A novel approach with optimized VMD and Vision Mamba |
| title_sort | sky images based photovoltaic power forecasting a novel approach with optimized vmd and vision mamba |
| topic | Photovoltaic power forecasting Vision Mamba Variational mode decomposition Snow ablation optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024012775 |
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