Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework
The variability in the spatiotemporal distribution of power generation is a significant challenge for accurately predicting renewable energy production patterns. Furthermore, numerous forms of unforeseen data contamination degrade the precision of forecasts since superfluous data points adversely af...
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Elsevier
2025-04-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174525000510 |
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| author | Md. Shadman Abid Razzaqul Ahshan Mohammed Al-Abri Rashid Al Abri |
| author_facet | Md. Shadman Abid Razzaqul Ahshan Mohammed Al-Abri Rashid Al Abri |
| author_sort | Md. Shadman Abid |
| collection | DOAJ |
| description | The variability in the spatiotemporal distribution of power generation is a significant challenge for accurately predicting renewable energy production patterns. Furthermore, numerous forms of unforeseen data contamination degrade the precision of forecasts since superfluous data points adversely affect the regression model. In this context, a novel robust deep learning model, termed the Convolutional Neural Network-Bidirectional Long Short-Term Memory model with spatiotemporal attention mechanism (CNN-BiLSTM-STA), is developed in this study. The suggested model integrates the feature extraction expertise of CNNs with the sequence modeling proficiency of BiLSTM networks to capture spatial linkages and temporal interdependence adeptly. Moreover, the integrated spatiotemporal attention mechanism selectively focuses on significant spatial regions and time steps to enhance the prediction of spatiotemporal sequences of time-resolved grid data. The proposed architecture allows plant proprietors and system operators to obtain accurate predictions across extensive spatiotemporal patterns by eliminating the necessity for individual model fitting for each site/horizon or an additional data preprocessing phase before training. In addition, the Correntropy-based training criterion is employed to ensure the robustness of the recommended method against various types of data contamination, including data incompletion, Gaussian noises, outliers, and a mixed combination of disturbances. Furthermore, the Partial Reinforcement Optimization technique is applied to optimize the hyperparameters of the proposed model. The suggested framework incorporates numerous photovoltaic installations in Arizona and wind power installations in Texas to provide concurrent forecasts for multiple periods. The efficacy of the suggested forecasting model is evaluated by comparing it with three state-of-the-art methods. Numerical findings demonstrate that the proposed model surpasses other methods by successfully integrating spatial and temporal characteristics. |
| format | Article |
| id | doaj-art-eeeb21d978064e5da2a241db40d902fe |
| institution | OA Journals |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-eeeb21d978064e5da2a241db40d902fe2025-08-20T02:31:56ZengElsevierEnergy Conversion and Management: X2590-17452025-04-012610091910.1016/j.ecmx.2025.100919Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention FrameworkMd. Shadman Abid0Razzaqul Ahshan1Mohammed Al-Abri2Rashid Al Abri3Nanotechnology Research Center, Sultan Qaboos University, Al-Khoud, 123, OmanDepartment of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Al-Khoud, 123, Oman; Corresponding author.Nanotechnology Research Center, Sultan Qaboos University, Al-Khoud, 123, Oman; Department of Petroleum and Chemical Engineering, College of Engineering, Sultan Qaboos University, Al-Khoud, 123, OmanDepartment of Electrical and Computer Engineering, College of Engineering and Architecture, University of Nizwa, Birkat Al Mawz, OmanThe variability in the spatiotemporal distribution of power generation is a significant challenge for accurately predicting renewable energy production patterns. Furthermore, numerous forms of unforeseen data contamination degrade the precision of forecasts since superfluous data points adversely affect the regression model. In this context, a novel robust deep learning model, termed the Convolutional Neural Network-Bidirectional Long Short-Term Memory model with spatiotemporal attention mechanism (CNN-BiLSTM-STA), is developed in this study. The suggested model integrates the feature extraction expertise of CNNs with the sequence modeling proficiency of BiLSTM networks to capture spatial linkages and temporal interdependence adeptly. Moreover, the integrated spatiotemporal attention mechanism selectively focuses on significant spatial regions and time steps to enhance the prediction of spatiotemporal sequences of time-resolved grid data. The proposed architecture allows plant proprietors and system operators to obtain accurate predictions across extensive spatiotemporal patterns by eliminating the necessity for individual model fitting for each site/horizon or an additional data preprocessing phase before training. In addition, the Correntropy-based training criterion is employed to ensure the robustness of the recommended method against various types of data contamination, including data incompletion, Gaussian noises, outliers, and a mixed combination of disturbances. Furthermore, the Partial Reinforcement Optimization technique is applied to optimize the hyperparameters of the proposed model. The suggested framework incorporates numerous photovoltaic installations in Arizona and wind power installations in Texas to provide concurrent forecasts for multiple periods. The efficacy of the suggested forecasting model is evaluated by comparing it with three state-of-the-art methods. Numerical findings demonstrate that the proposed model surpasses other methods by successfully integrating spatial and temporal characteristics.http://www.sciencedirect.com/science/article/pii/S2590174525000510Renewable energySolar energyWind energySpatiotemporal forecastingBiLSTMPartial reinforcement optimization |
| spellingShingle | Md. Shadman Abid Razzaqul Ahshan Mohammed Al-Abri Rashid Al Abri Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework Energy Conversion and Management: X Renewable energy Solar energy Wind energy Spatiotemporal forecasting BiLSTM Partial reinforcement optimization |
| title | Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework |
| title_full | Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework |
| title_fullStr | Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework |
| title_full_unstemmed | Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework |
| title_short | Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework |
| title_sort | spatiotemporal forecasting of solar and wind energy production a robust deep learning model with attention framework |
| topic | Renewable energy Solar energy Wind energy Spatiotemporal forecasting BiLSTM Partial reinforcement optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590174525000510 |
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