Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP inspired optimization algorithm
Ground source heat pump (GSHP) has recently gained a great attention because of its efficient utilization of geothermal energy for building cooling and heating. However, GSHP systems face significant challenges in real-time applications because of thermal imbalances, and fluctuating cooling loads, d...
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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/S2590123025017955 |
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| author | Sana Qaiyum Kashif Irshad Mohamed E. Zayed Salem Algarni Talal Alqahtani Asif Irshad Khan |
| author_facet | Sana Qaiyum Kashif Irshad Mohamed E. Zayed Salem Algarni Talal Alqahtani Asif Irshad Khan |
| author_sort | Sana Qaiyum |
| collection | DOAJ |
| description | Ground source heat pump (GSHP) has recently gained a great attention because of its efficient utilization of geothermal energy for building cooling and heating. However, GSHP systems face significant challenges in real-time applications because of thermal imbalances, and fluctuating cooling loads, demanding a long-term performance prediction mechanism. This study proposed an innovative predictive hybrid strategy leveraging Spider Wasp Optimization with the Spiking Recurrent Neural Network (SWO-SRNN). The SWO is utilized to refine the parameters of SRNN, reducing the model’s loss and training complications. The developed model begins with the collection of datasets representing the parametric modeling of GSHP. Consequently, Emperor Penguins Colony (EPC) optimization algorithm was also employed for selecting the essential features, which reduces the data dimensionality and assists the predictive algorithm to focus on important features in its training phase. Furthermore, the proposed SWO-SRNN was trained using the selected features to predict the ground temperature and Coefficient of Performance (COP), which enables to make appropriate actions to optimize the functioning of GSHP. Finally, statistical analysis was used to evaluate the robustness of the developed SWO-SRNN models. The statistical results prove the effectiveness and superiority of the proposed SWO-SRNN method compared the standalone SRNN model for performance prediction of the GSHP. The simulated results revealed that the deterministic coefficient (R2) and RMSE of the predicted ground temperature were 0.89 and 0.14 for SWO-SRNN, compared to 0.82 and 0.151 for the classical SRNN, respectively. Therefore, SWO-SRNN demonstrated superior predictive accuracy, establishing itself as a highly effective optimization tool for forecasting the energetic performance of GSHPs. These findings highlight the potential of the proposed method to be further explored and extended for real-world applications and future research in intelligent energy systems. |
| format | Article |
| id | doaj-art-46d223ff9b6341d08b0ec0c29c6d9172 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-46d223ff9b6341d08b0ec0c29c6d91722025-08-20T03:22:33ZengElsevierResults in Engineering2590-12302025-09-012710572410.1016/j.rineng.2025.105724Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP inspired optimization algorithmSana Qaiyum0Kashif Irshad1Mohamed E. Zayed2Salem Algarni3Talal Alqahtani4Asif Irshad Khan5Computer Science and Engineering Department, Siddhartha Institute of Engineering and Technology, Hyderabad, IndiaInterdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; Mechanical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaInterdisciplinary Research Center for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaMechanical Engineering Department, College of Engineering, King Khalid University, Abha 9004, Saudi Arabia; Center for Engineering and Technology Innovation, King Khalid University, Abha 61421, Saudi ArabiaMechanical Engineering Department, College of Engineering, King Khalid University, Abha 9004, Saudi Arabia; Center for Engineering and Technology Innovation, King Khalid University, Abha 61421, Saudi ArabiaDepartment of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, India; Corresponding author.Ground source heat pump (GSHP) has recently gained a great attention because of its efficient utilization of geothermal energy for building cooling and heating. However, GSHP systems face significant challenges in real-time applications because of thermal imbalances, and fluctuating cooling loads, demanding a long-term performance prediction mechanism. This study proposed an innovative predictive hybrid strategy leveraging Spider Wasp Optimization with the Spiking Recurrent Neural Network (SWO-SRNN). The SWO is utilized to refine the parameters of SRNN, reducing the model’s loss and training complications. The developed model begins with the collection of datasets representing the parametric modeling of GSHP. Consequently, Emperor Penguins Colony (EPC) optimization algorithm was also employed for selecting the essential features, which reduces the data dimensionality and assists the predictive algorithm to focus on important features in its training phase. Furthermore, the proposed SWO-SRNN was trained using the selected features to predict the ground temperature and Coefficient of Performance (COP), which enables to make appropriate actions to optimize the functioning of GSHP. Finally, statistical analysis was used to evaluate the robustness of the developed SWO-SRNN models. The statistical results prove the effectiveness and superiority of the proposed SWO-SRNN method compared the standalone SRNN model for performance prediction of the GSHP. The simulated results revealed that the deterministic coefficient (R2) and RMSE of the predicted ground temperature were 0.89 and 0.14 for SWO-SRNN, compared to 0.82 and 0.151 for the classical SRNN, respectively. Therefore, SWO-SRNN demonstrated superior predictive accuracy, establishing itself as a highly effective optimization tool for forecasting the energetic performance of GSHPs. These findings highlight the potential of the proposed method to be further explored and extended for real-world applications and future research in intelligent energy systems.http://www.sciencedirect.com/science/article/pii/S2590123025017955Artificial intelligenceGround source heat pumpSpider wasp optimizationSpiking recurrent neural networkGround temperature distribution prediction |
| spellingShingle | Sana Qaiyum Kashif Irshad Mohamed E. Zayed Salem Algarni Talal Alqahtani Asif Irshad Khan Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP inspired optimization algorithm Results in Engineering Artificial intelligence Ground source heat pump Spider wasp optimization Spiking recurrent neural network Ground temperature distribution prediction |
| title | Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP inspired optimization algorithm |
| title_full | Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP inspired optimization algorithm |
| title_fullStr | Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP inspired optimization algorithm |
| title_full_unstemmed | Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP inspired optimization algorithm |
| title_short | Benchmarking reinforcement learning and accurate modeling of ground source heat pump systems: Intelligent strategy using spiking recurrent neural network combined with spider WASP inspired optimization algorithm |
| title_sort | benchmarking reinforcement learning and accurate modeling of ground source heat pump systems intelligent strategy using spiking recurrent neural network combined with spider wasp inspired optimization algorithm |
| topic | Artificial intelligence Ground source heat pump Spider wasp optimization Spiking recurrent neural network Ground temperature distribution prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025017955 |
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