Multistep PV power forecasting using deep learning models and the reptile search algorithm
Forecasting Photovoltaic (PV) power output is a key challenge in renewable energy systems, particularly for short- to mid-term operational planning. Accurate multi-step PV forecasting supports efficient energy scheduling, grid stability, and integration of solar resources. This study investigates th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025023370 |
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| author | Sameer Al-Dahidi Hussein Alahmer Bilal Rinchi Abdullah Bani-Abdullah Mohammad Alrbai Osama Ayadi Loiy Al-Ghussain |
| author_facet | Sameer Al-Dahidi Hussein Alahmer Bilal Rinchi Abdullah Bani-Abdullah Mohammad Alrbai Osama Ayadi Loiy Al-Ghussain |
| author_sort | Sameer Al-Dahidi |
| collection | DOAJ |
| description | Forecasting Photovoltaic (PV) power output is a key challenge in renewable energy systems, particularly for short- to mid-term operational planning. Accurate multi-step PV forecasting supports efficient energy scheduling, grid stability, and integration of solar resources. This study investigates the performance of three advanced deep learning models: Temporal Convolutional Network (TCN), Minimal Gated Unit (MGU), and Temporal Fusion Transformer (TFT), applied to one-day-ahead and three-day-ahead PV power forecasting. The Reptile Search Algorithm (RSA), a novel metaheuristic optimizer, is employed for hyperparameter tuning. Results show that TFT consistently outperforms the other models, achieving Root Mean Square Error (RMSE) values of 6.256 kWh and 8.353 kWh and coefficient of determination (R²) scores of 98.92 % and 98.07 % for the one-day and three-day forecasts, respectively. RSA optimization yields significant performance gains, reducing TFT’s RMSE by 44.57 % and 42.43 % relative to its non-optimized baseline. While MGU had the weakest overall performance, particularly under summer conditions and zero-generation periods, it performed better in the one-day-ahead setting than TCN did in the three-day-ahead scenario. Further, RSA was benchmarked against the Whale Optimization Algorithm (WOA), Gray Wolf Optimizer (GWO), and Constrained Particle Swarm Optimization (CPSO), and consistently outperformed all three across all models in both forecasting horizons. However, this is the first study to evaluate MGU in the context of PV forecasting, and its performance may vary under different case studies. It is shown that TFT and RSA offer superior accuracy and generalization across forecast horizons. These findings support the broader adoption and further benchmarking of TFT future research. |
| format | Article |
| id | doaj-art-0453da21528a4e208d8f7282efea0080 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-0453da21528a4e208d8f7282efea00802025-08-20T03:50:54ZengElsevierResults in Engineering2590-12302025-09-012710626510.1016/j.rineng.2025.106265Multistep PV power forecasting using deep learning models and the reptile search algorithmSameer Al-Dahidi0Hussein Alahmer1Bilal Rinchi2Abdullah Bani-Abdullah3Mohammad Alrbai4Osama Ayadi5Loiy Al-Ghussain6Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, 11180, Jordan; Corresponding authors.Department of Automated Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt, 19117, Jordan; Corresponding authors.Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman, 11180, Jordan; Corresponding authors.Department of Electrical Engineering, Faculty of Engineering, Applied Science Private University, Amman, 11931, JordanDepartment of Mechanical Engineering, School of Engineering, The University of Jordan, Amman, 11942, JordanDepartment of Mechanical Engineering, School of Engineering, The University of Jordan, Amman, 11942, JordanSystems Assessment Center, Energy Systems and Infrastructure Analysis Division, Argonne National Laboratory, Lemont, IL, 60439, USAForecasting Photovoltaic (PV) power output is a key challenge in renewable energy systems, particularly for short- to mid-term operational planning. Accurate multi-step PV forecasting supports efficient energy scheduling, grid stability, and integration of solar resources. This study investigates the performance of three advanced deep learning models: Temporal Convolutional Network (TCN), Minimal Gated Unit (MGU), and Temporal Fusion Transformer (TFT), applied to one-day-ahead and three-day-ahead PV power forecasting. The Reptile Search Algorithm (RSA), a novel metaheuristic optimizer, is employed for hyperparameter tuning. Results show that TFT consistently outperforms the other models, achieving Root Mean Square Error (RMSE) values of 6.256 kWh and 8.353 kWh and coefficient of determination (R²) scores of 98.92 % and 98.07 % for the one-day and three-day forecasts, respectively. RSA optimization yields significant performance gains, reducing TFT’s RMSE by 44.57 % and 42.43 % relative to its non-optimized baseline. While MGU had the weakest overall performance, particularly under summer conditions and zero-generation periods, it performed better in the one-day-ahead setting than TCN did in the three-day-ahead scenario. Further, RSA was benchmarked against the Whale Optimization Algorithm (WOA), Gray Wolf Optimizer (GWO), and Constrained Particle Swarm Optimization (CPSO), and consistently outperformed all three across all models in both forecasting horizons. However, this is the first study to evaluate MGU in the context of PV forecasting, and its performance may vary under different case studies. It is shown that TFT and RSA offer superior accuracy and generalization across forecast horizons. These findings support the broader adoption and further benchmarking of TFT future research.http://www.sciencedirect.com/science/article/pii/S2590123025023370Reptile search algorithmPhotovoltaic power forecastingDeep learningOptimizationMultistep forecasting |
| spellingShingle | Sameer Al-Dahidi Hussein Alahmer Bilal Rinchi Abdullah Bani-Abdullah Mohammad Alrbai Osama Ayadi Loiy Al-Ghussain Multistep PV power forecasting using deep learning models and the reptile search algorithm Results in Engineering Reptile search algorithm Photovoltaic power forecasting Deep learning Optimization Multistep forecasting |
| title | Multistep PV power forecasting using deep learning models and the reptile search algorithm |
| title_full | Multistep PV power forecasting using deep learning models and the reptile search algorithm |
| title_fullStr | Multistep PV power forecasting using deep learning models and the reptile search algorithm |
| title_full_unstemmed | Multistep PV power forecasting using deep learning models and the reptile search algorithm |
| title_short | Multistep PV power forecasting using deep learning models and the reptile search algorithm |
| title_sort | multistep pv power forecasting using deep learning models and the reptile search algorithm |
| topic | Reptile search algorithm Photovoltaic power forecasting Deep learning Optimization Multistep forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025023370 |
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