Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many...
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| Format: | Artikel |
| Sprache: | English |
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Taylor & Francis Group
2023-12-01
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| Schriftenreihe: | Engineering Applications of Computational Fluid Mechanics |
| Schlagworte: | |
| Online Zugang: | https://www.tandfonline.com/doi/10.1080/19942060.2023.2226725 |
| _version_ | 1827594996173242368 |
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| author | Nidhal Ben Khedher Azfarizal Mukhtar Ahmad Shah Hizam Md Yasir Nima Khalilpoor Loke Kok Foong Binh Nguyen Le Hasan Yildizhan |
| author_facet | Nidhal Ben Khedher Azfarizal Mukhtar Ahmad Shah Hizam Md Yasir Nima Khalilpoor Loke Kok Foong Binh Nguyen Le Hasan Yildizhan |
| author_sort | Nidhal Ben Khedher |
| collection | DOAJ |
| description | The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings’ heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R2 and RMSE). Model performance of PSO-MLP is shown by R2 amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R2 amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance. |
| first_indexed | 2024-03-09T02:46:20Z |
| format | Article |
| id | doaj.art-7b3ed5cb821140eabe247244c62750e1 |
| institution | Directory Open Access Journal |
| issn | 1994-2060 1997-003X |
| language | English |
| last_indexed | 2024-03-09T02:46:20Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj.art-7b3ed5cb821140eabe247244c62750e12023-12-05T16:53:44ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2023-12-0117110.1080/19942060.2023.2226725Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutionsNidhal Ben Khedher0Azfarizal Mukhtar1Ahmad Shah Hizam Md Yasir2Nima Khalilpoor3Loke Kok Foong4Binh Nguyen Le5Hasan Yildizhan6Department of Mechanical Engineering, College of Engineering, University of Ha’il, Ha’il, Saudi ArabiaInstitute of Sustainable Energy, Universiti Tenaga Nasional, Kajang, MalaysiaFaculty of Resilience, Rabdan Academy, Abu Dhabi, UAEDepartment of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, IranInstitute of Research and Development, Duy Tan University, Da Nang, VietnamInstitute of Research and Development, Duy Tan University, Da Nang, VietnamDepartment of Energy Systems Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, TurkeyThe attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings’ heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R2 and RMSE). Model performance of PSO-MLP is shown by R2 amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R2 amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance.https://www.tandfonline.com/doi/10.1080/19942060.2023.2226725Green buildingsheat lossharmony searchparticle swarm optimisationartificial neural network |
| spellingShingle | Nidhal Ben Khedher Azfarizal Mukhtar Ahmad Shah Hizam Md Yasir Nima Khalilpoor Loke Kok Foong Binh Nguyen Le Hasan Yildizhan Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions Engineering Applications of Computational Fluid Mechanics Green buildings heat loss harmony search particle swarm optimisation artificial neural network |
| title | Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions |
| title_full | Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions |
| title_fullStr | Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions |
| title_full_unstemmed | Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions |
| title_short | Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions |
| title_sort | approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions |
| topic | Green buildings heat loss harmony search particle swarm optimisation artificial neural network |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2023.2226725 |
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