Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heating
This study investigates the energy performance of a combined solar underfloor heating and domestic hot water (DHW) system using an innovative approach that combines experimental data and mathematical modeling. The PieceWise Affine Auto-Regressive eXogenous (PWARX) model was employed to identify disc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25001959 |
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| author | Yassine Bouguergour Sayeh Menhoudj Abderrahmane Mejedoub Mokhtari Karim Dehina Abdelatif Zairi Romain Mege Mohammed-Hichem Benzaama |
| author_facet | Yassine Bouguergour Sayeh Menhoudj Abderrahmane Mejedoub Mokhtari Karim Dehina Abdelatif Zairi Romain Mege Mohammed-Hichem Benzaama |
| author_sort | Yassine Bouguergour |
| collection | DOAJ |
| description | This study investigates the energy performance of a combined solar underfloor heating and domestic hot water (DHW) system using an innovative approach that combines experimental data and mathematical modeling. The PieceWise Affine Auto-Regressive eXogenous (PWARX) model was employed to identify discrete operational states and optimize the system’s performance. Three configurations were analyzed under winter conditions: (1) the solar underfloor heating system achieved 130 % energy coverage, maintaining stable temperatures between 17 °C and 19.5 °C; (2) the DHW system with a 300 L storage tank recorded a 71 % coverage, optimizing circulator operation and thermal energy storage; and (3) the combined system demonstrated synergy between the components, balancing energy production with a minimum coverage of 45 %.The PWARX model identified four distinct operational states, correlating solar radiation with the system’s thermal response, providing insights for energy management and system optimization. The findings underline the potential of the PWARX model to enhance the design and efficiency of solar thermal systems. This study contributes to the energy transition by proposing effective and adaptable solutions for maximizing solar energy utilization in the residential sector. |
| format | Article |
| id | doaj-art-d47421411e2d47c88bcdbade397e9a6a |
| institution | DOAJ |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-d47421411e2d47c88bcdbade397e9a6a2025-08-20T03:18:16ZengElsevierCase Studies in Thermal Engineering2214-157X2025-05-016910593510.1016/j.csite.2025.105935Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heatingYassine Bouguergour0Sayeh Menhoudj1Abderrahmane Mejedoub Mokhtari2Karim Dehina3Abdelatif Zairi4Romain Mege5Mohammed-Hichem Benzaama6Laboratoire de Matériaux Sols et Thermique (LMST), Faculté d’Architecture et Génie Civil, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouar, Bir El Djir, 31000, Oran, Algeria; Corresponding author. Laboratoire de Matériaux Sols et Thermique (LMST), Faculté d’Architecture et Génie Civil, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouar, Bir El Djir, 31000, Oran, Algeria.Laboratoire de Matériaux Sols et Thermique (LMST), Faculté d’Architecture et Génie Civil, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouar, Bir El Djir, 31000, Oran, Algeria; Université d'Oran 2, Mohamed Ben Ahmed, AlgeriaLaboratoire de Matériaux Sols et Thermique (LMST), Faculté d’Architecture et Génie Civil, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouar, Bir El Djir, 31000, Oran, AlgeriaLaboratoire de Matériaux Sols et Thermique (LMST), Faculté d’Architecture et Génie Civil, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouar, Bir El Djir, 31000, Oran, AlgeriaLaboratoire de Matériaux Sols et Thermique (LMST), Faculté d’Architecture et Génie Civil, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouar, Bir El Djir, 31000, Oran, AlgeriaInstitut de Recherche, ESTP, 28 Avenue du Président Wilson, F-94230, Cachan, FranceInstitut de Recherche, ESTP, 28 Avenue du Président Wilson, F-94230, Cachan, FranceThis study investigates the energy performance of a combined solar underfloor heating and domestic hot water (DHW) system using an innovative approach that combines experimental data and mathematical modeling. The PieceWise Affine Auto-Regressive eXogenous (PWARX) model was employed to identify discrete operational states and optimize the system’s performance. Three configurations were analyzed under winter conditions: (1) the solar underfloor heating system achieved 130 % energy coverage, maintaining stable temperatures between 17 °C and 19.5 °C; (2) the DHW system with a 300 L storage tank recorded a 71 % coverage, optimizing circulator operation and thermal energy storage; and (3) the combined system demonstrated synergy between the components, balancing energy production with a minimum coverage of 45 %.The PWARX model identified four distinct operational states, correlating solar radiation with the system’s thermal response, providing insights for energy management and system optimization. The findings underline the potential of the PWARX model to enhance the design and efficiency of solar thermal systems. This study contributes to the energy transition by proposing effective and adaptable solutions for maximizing solar energy utilization in the residential sector.http://www.sciencedirect.com/science/article/pii/S2214157X25001959Solar thermalDirect solar floorStorage tankManagement strategyMachine learningIdentification |
| spellingShingle | Yassine Bouguergour Sayeh Menhoudj Abderrahmane Mejedoub Mokhtari Karim Dehina Abdelatif Zairi Romain Mege Mohammed-Hichem Benzaama Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heating Case Studies in Thermal Engineering Solar thermal Direct solar floor Storage tank Management strategy Machine learning Identification |
| title | Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heating |
| title_full | Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heating |
| title_fullStr | Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heating |
| title_full_unstemmed | Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heating |
| title_short | Experimental and machine learning-based identification of a solar thermal system for domestic hot water and direct solar floor heating |
| title_sort | experimental and machine learning based identification of a solar thermal system for domestic hot water and direct solar floor heating |
| topic | Solar thermal Direct solar floor Storage tank Management strategy Machine learning Identification |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25001959 |
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