Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systems
This study addresses the integration of machine learning (ML) and artificial intelligence (AI) for optimizing photovoltaic-thermal (PVT) systems. While ML modeling has become prevalent in this field, a significant gap remains in combining ML with AI-based optimization and decision-making methods to...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X2500111X |
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| author | Yongxin Li Ali Basem As'ad Alizadeh Pradeep Kumar Singh Saurav Dixit Hanaa Kadhim Abdulaali Rifaqat Ali Pancham Cajla Husam Rajab Kaouther Ghachem |
| author_facet | Yongxin Li Ali Basem As'ad Alizadeh Pradeep Kumar Singh Saurav Dixit Hanaa Kadhim Abdulaali Rifaqat Ali Pancham Cajla Husam Rajab Kaouther Ghachem |
| author_sort | Yongxin Li |
| collection | DOAJ |
| description | This study addresses the integration of machine learning (ML) and artificial intelligence (AI) for optimizing photovoltaic-thermal (PVT) systems. While ML modeling has become prevalent in this field, a significant gap remains in combining ML with AI-based optimization and decision-making methods to enhance PVT performance. This research introduces a four-step hybrid framework that integrates data analysis, ML modeling, multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to achieve comprehensive PVT system optimization. In a case study of a phase change material (PCM)-based PVT system, a GMDH-type ANN model was applied to predict electrical power (EP), thermal power (TP), and entropy generation (EG) based on inputs including PCM melting temperature, PCM thickness, solar radiation, and ambient temperature. Results demonstrated the model's high accuracy, with R2 values exceeding 0.998 across objectives. MOO based on GMDH-NN models using the novel multi-objective thermal exchange optimization (MOTEO) algorithm revealed trade-offs: maximizing EP and TP increased EG, highlighting inherent inefficiencies. The Pareto optimal points highlighted the significant influence of environmental conditions and PCM characteristics, with an ambient temperature of 10 °C, a PCM melting temperature of 25 °C, and a PCM thickness of 1.5 cm yielding the majority of optimal outputs. MCDM through PROMETHEE provided design flexibility, allowing weighted objectives to support various design scenarios. This framework underscores ML and AI's potential to elevate PVT system performance, establishing a foundation for future renewable energy technologies. |
| format | Article |
| id | doaj-art-ba1030b3916449f89f0afc26bfa01c7d |
| institution | OA Journals |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-ba1030b3916449f89f0afc26bfa01c7d2025-08-20T01:57:36ZengElsevierCase Studies in Thermal Engineering2214-157X2025-04-016810585110.1016/j.csite.2025.105851Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systemsYongxin Li0Ali Basem1As'ad Alizadeh2Pradeep Kumar Singh3Saurav Dixit4Hanaa Kadhim Abdulaali5Rifaqat Ali6Pancham Cajla7Husam Rajab8Kaouther Ghachem9School of Computer Science, Xijing University, Xi'an, Shaanxi, 710123, ChinaFaculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, IraqDepartment of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, Iraq; Corresponding author.Department of Mechanical Engineering, Institute of Engineering and Technology, GLA University, Mathura (U.P.), IndiaCentre of Research Impact and Outcome, Chitkara University, Rajpura-140417, Punjab, IndiaDepartment of Chemical Engineering, University of Technology- Iraq, Baghdad, IraqDepartment of Mathematics, Applied College in Mohayil Asir, King Khalid University, Abha, Saudi ArabiaChitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103, IndiaCollege of Engineering, Department of Mechanical Engineering, Najran University, King Abdulaziz Road, P.O Box 1988, Najran, Kingdom of Saudi ArabiaDepartment of Industrial & Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi ArabiaThis study addresses the integration of machine learning (ML) and artificial intelligence (AI) for optimizing photovoltaic-thermal (PVT) systems. While ML modeling has become prevalent in this field, a significant gap remains in combining ML with AI-based optimization and decision-making methods to enhance PVT performance. This research introduces a four-step hybrid framework that integrates data analysis, ML modeling, multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to achieve comprehensive PVT system optimization. In a case study of a phase change material (PCM)-based PVT system, a GMDH-type ANN model was applied to predict electrical power (EP), thermal power (TP), and entropy generation (EG) based on inputs including PCM melting temperature, PCM thickness, solar radiation, and ambient temperature. Results demonstrated the model's high accuracy, with R2 values exceeding 0.998 across objectives. MOO based on GMDH-NN models using the novel multi-objective thermal exchange optimization (MOTEO) algorithm revealed trade-offs: maximizing EP and TP increased EG, highlighting inherent inefficiencies. The Pareto optimal points highlighted the significant influence of environmental conditions and PCM characteristics, with an ambient temperature of 10 °C, a PCM melting temperature of 25 °C, and a PCM thickness of 1.5 cm yielding the majority of optimal outputs. MCDM through PROMETHEE provided design flexibility, allowing weighted objectives to support various design scenarios. This framework underscores ML and AI's potential to elevate PVT system performance, establishing a foundation for future renewable energy technologies.http://www.sciencedirect.com/science/article/pii/S2214157X2500111XPhotovoltaic thermal systemMachine learningMulti-objective thermal exchange optimizationMulti-criteria decision-makingMathematical modelingRenewable energy optimization |
| spellingShingle | Yongxin Li Ali Basem As'ad Alizadeh Pradeep Kumar Singh Saurav Dixit Hanaa Kadhim Abdulaali Rifaqat Ali Pancham Cajla Husam Rajab Kaouther Ghachem Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systems Case Studies in Thermal Engineering Photovoltaic thermal system Machine learning Multi-objective thermal exchange optimization Multi-criteria decision-making Mathematical modeling Renewable energy optimization |
| title | Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systems |
| title_full | Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systems |
| title_fullStr | Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systems |
| title_full_unstemmed | Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systems |
| title_short | Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systems |
| title_sort | synergizing neural networks with multi objective thermal exchange optimization and promethee decision making to improve pcm based photovoltaic thermal systems |
| topic | Photovoltaic thermal system Machine learning Multi-objective thermal exchange optimization Multi-criteria decision-making Mathematical modeling Renewable energy optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X2500111X |
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