Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study
This study introduces an approach to improving the utilization of solar energy in facilities by integrating advanced machine learning (ML) techniques into solar power scheduling. Traditional methods, often constrained by static schedules, fail to adequately adapt to the inherently dynamic and interm...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Renewable and Sustainable Energy Transition |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667095X25000133 |
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| author | Hussam J. Khasawneh Waseem M. Al-Khatib Zaid A. Ghazal Ahmad M. Al-Hadi Zaid M. Arabiyat Osama Habahbeh |
| author_facet | Hussam J. Khasawneh Waseem M. Al-Khatib Zaid A. Ghazal Ahmad M. Al-Hadi Zaid M. Arabiyat Osama Habahbeh |
| author_sort | Hussam J. Khasawneh |
| collection | DOAJ |
| description | This study introduces an approach to improving the utilization of solar energy in facilities by integrating advanced machine learning (ML) techniques into solar power scheduling. Traditional methods, often constrained by static schedules, fail to adequately adapt to the inherently dynamic and intermittent nature of solar energy. Our approach overcomes these limitations by employing ML algorithms to accurately predict solar generation patterns, enabling more efficient scheduling of electrical appliances. This methodology was applied to a facility equipped with a 5 kW photovoltaic system, resulting in a significant reduction in grid dependency by more than 26%. This marked decrease in grid imports underscores the effectiveness of our approach in optimizing solar energy use, particularly in settings where traditional scheduling methods fall short. The study demonstrates the practical benefits of ML in managing solar energy resources to reduce dependence on conventional power grids, thus contributing to more sustainable energy practices. The findings of this research have far-reaching implications, suggesting a notable advancement in solar energy management towards more adaptive, data-driven solutions and paving the way for broader applications in various sectors seeking to maximize renewable energy use. |
| format | Article |
| id | doaj-art-d149b8b56aea4f71907e938693f4d041 |
| institution | OA Journals |
| issn | 2667-095X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Renewable and Sustainable Energy Transition |
| spelling | doaj-art-d149b8b56aea4f71907e938693f4d0412025-08-20T01:52:04ZengElsevierRenewable and Sustainable Energy Transition2667-095X2025-06-01710011410.1016/j.rset.2025.100114Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case studyHussam J. Khasawneh0Waseem M. Al-Khatib1Zaid A. Ghazal2Ahmad M. Al-Hadi3Zaid M. Arabiyat4Osama Habahbeh5Department of Mechatronics Engineering, The University of Jordan, Amman, 11942, Jordan; Department of Electrical Engineering, Al Hussein Technical University, Amman, 11831, Jordan; Corresponding author at: Department of Mechatronics Engineering, The University of Jordan, Amman, 11942, Jordan.Department of Mechatronics Engineering, The University of Jordan, Amman, 11942, JordanComputer and Information Science Department, University of Michigan-Dearborn, Dearborn, MI, 48128, USADepartment of Mechatronics Engineering, The University of Jordan, Amman, 11942, JordanDepartment of Mechatronics Engineering, The University of Jordan, Amman, 11942, JordanDepartment of Mechatronics Engineering, The University of Jordan, Amman, 11942, JordanThis study introduces an approach to improving the utilization of solar energy in facilities by integrating advanced machine learning (ML) techniques into solar power scheduling. Traditional methods, often constrained by static schedules, fail to adequately adapt to the inherently dynamic and intermittent nature of solar energy. Our approach overcomes these limitations by employing ML algorithms to accurately predict solar generation patterns, enabling more efficient scheduling of electrical appliances. This methodology was applied to a facility equipped with a 5 kW photovoltaic system, resulting in a significant reduction in grid dependency by more than 26%. This marked decrease in grid imports underscores the effectiveness of our approach in optimizing solar energy use, particularly in settings where traditional scheduling methods fall short. The study demonstrates the practical benefits of ML in managing solar energy resources to reduce dependence on conventional power grids, thus contributing to more sustainable energy practices. The findings of this research have far-reaching implications, suggesting a notable advancement in solar energy management towards more adaptive, data-driven solutions and paving the way for broader applications in various sectors seeking to maximize renewable energy use.http://www.sciencedirect.com/science/article/pii/S2667095X25000133Solar energy optimizationMachine learning (ML)Artificial intelligence (AI)Consumption schedulingLoad shiftingForecast-based scheduling |
| spellingShingle | Hussam J. Khasawneh Waseem M. Al-Khatib Zaid A. Ghazal Ahmad M. Al-Hadi Zaid M. Arabiyat Osama Habahbeh Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study Renewable and Sustainable Energy Transition Solar energy optimization Machine learning (ML) Artificial intelligence (AI) Consumption scheduling Load shifting Forecast-based scheduling |
| title | Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study |
| title_full | Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study |
| title_fullStr | Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study |
| title_full_unstemmed | Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study |
| title_short | Optimizing solar energy utilization in facilities using machine learning-based scheduling techniques: A case study |
| title_sort | optimizing solar energy utilization in facilities using machine learning based scheduling techniques a case study |
| topic | Solar energy optimization Machine learning (ML) Artificial intelligence (AI) Consumption scheduling Load shifting Forecast-based scheduling |
| url | http://www.sciencedirect.com/science/article/pii/S2667095X25000133 |
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