Yazar "Al-Bahrani, Mohammed" seçeneğine göre listele

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    Appraisal of energy loss reduction in green buildings using large-scale experiments compiled with swarm intelligent solutions
    (Elsevier, 2023) Moayedi, Hossein; Yildizhan, Hasan; Al-Bahrani, Mohammed; Le Van, Bao
    Today, the issue of energy efficiency is a major one in global politics. The external environment, particularly the wind speed and outside air temperature, determines the thermal burden the cold outside air places on a building's interior. The heat load of a building is influenced by several factors, including the wall's heat transfer coefficients (W/mK), the coating material (W/mK), the inside temperature (degrees C), and the outside temperature (degrees C), and the temperature of external surface (degrees C). In this investigation, we undertake a comprehensive assessment, evaluation, and comparing the performance of two unique artificial approaches (BSA and COA) utilized for anticipating heat loss in green buildings; the optimum way is then identified depending on the R-2 and RMSE criteria. The outcomes demonstrate that BSA and COA have R-2 values of (0.97038 and 0.90158) and (0.9919 and 0.94239) in the training and testing phases. Additionally, the RMSE values for BSA and COA in the training and testing stages are (0.02541 and 0.08616) and (0.01336 and 0.06662), correspondingly. Also, the estimated MAEs (0.019055 and 0.0097193) denote a low level of training error for both methods. Regarding R-2, RMSE and MAE values, the COA predicts energy loss more accurately.
  • [ X ]
    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.