Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF)
The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliabi...
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MDPI AG
2025-08-01
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| author | Haytham Elmousalami Felix Kin Peng Hui Aljawharah A. Alnaser |
| author_facet | Haytham Elmousalami Felix Kin Peng Hui Aljawharah A. Alnaser |
| author_sort | Haytham Elmousalami |
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| description | The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R<sup>2</sup> of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R<sup>2</sup> of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO<sub>2</sub> emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. |
| format | Article |
| id | doaj-art-21278bfb7b1c4ffcb6f2574a67c2bbfe |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-21278bfb7b1c4ffcb6f2574a67c2bbfe2025-08-20T03:36:35ZengMDPI AGBuildings2075-53092025-08-011515278510.3390/buildings15152785Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF)Haytham Elmousalami0Felix Kin Peng Hui1Aljawharah A. Alnaser2Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC 3010, AustraliaDepartment of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC 3010, AustraliaDepartment of Architecture and Building Science, College of Architecture and Planning, King Saud University, Riyadh 11421, Saudi ArabiaThe transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R<sup>2</sup> of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R<sup>2</sup> of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO<sub>2</sub> emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure.https://www.mdpi.com/2075-5309/15/15/2785sustainable artificial intelligencesolar power forecastingenergy supply optimizationsmart cities energy managementrenewable energy predictionBenban Solar Park |
| spellingShingle | Haytham Elmousalami Felix Kin Peng Hui Aljawharah A. Alnaser Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) Buildings sustainable artificial intelligence solar power forecasting energy supply optimization smart cities energy management renewable energy prediction Benban Solar Park |
| title | Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) |
| title_full | Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) |
| title_fullStr | Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) |
| title_full_unstemmed | Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) |
| title_short | Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF) |
| title_sort | enhancing smart and zero carbon cities through a hybrid cnn lstm algorithm for sustainable ai driven solar power forecasting sai spf |
| topic | sustainable artificial intelligence solar power forecasting energy supply optimization smart cities energy management renewable energy prediction Benban Solar Park |
| url | https://www.mdpi.com/2075-5309/15/15/2785 |
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