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    Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
    (Taylor & Francis Ltd, 2023) Ben Khedher, Nidhal; Mukhtar, Azfarizal; Yasir, Ahmad Shah Hizam Md; Khalilpoor, Nima; Foong, Loke Kok; Le, Binh Nguyen; Yildizhan, Hasan
    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 (R (2) and RMSE). Model performance of PSO-MLP is shown by R (2) 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 R (2) 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.
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    Öğe
    Green building?s heat loss reduction analysis through two novel hybrid approaches
    (Elsevier, 2023) Moayedi, Hossein; Yildizhan, Hasan; Aungkulanon, Pasura; Escorcia, Yulineth Cardenas; Al-Bahrani, Mohammed; Le, Binh Nguyen
    One of the key reasons for the performance discrepancy between a building's intended usage and the actual operation is Heat Loss, which describes a building's envelope efficiency during in-use circumstances. In this setting, the ANN models' use for energy analysis of green buildings has become more established. This research aims to anticipate the heat loss of green buildings utilizing two artificial neural network-based methodologies (ANN). In particular, TLBO and BBO are used and contrasted. Additionally, RMSE, MAE, and R2 are used to calculate an absolute error for predicting heat loss to gauge the accuracy of the findings. The suggested TLBO-MLP standard is a reliable method with a positive outcome (RMSE = 0.01012 and 0.05216, and R2 = 0.99536 and 0.9651). Also, according to the training error ranges of [-0.0006078, 0.01133] and [-0.00040708, 0.010181] and testing error ranges of [0.0004724, 0.068666] and [0.0021984, 0.057688] for BBO-MLP and TLBO-MLP, respectively, shows that the TLBO-MLP reaches the lower range of error and can predict the heat loss with higher accuracy and it could properly forecast the heat loss of building technologies. Even so, the BBO-MLP standard provides this research with satisfactory performance (R2 = 0.9943 and 0.95175, and RMSE = 0.01122 and 0.06112). To increase the precision of calculating the heat loss of buildings, specifically integrating them with optimization algorithms, further study is required.