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• W2024351726 abstract "Abstract In line with the growing global trend towards energy efficiency in buildings, this paper introduces a predictive model based on artificial neural network to rate the performance of windows in terms of saved heating and cooling loads. A four-story building representing the conventional type of residential apartments in Iran for four cities of Rasht, Ardabil, Yazd and Bandar-e- Abbas were selected for simulation. An artificial neural network model was developed based on ten variables of U-factor, solar heat gain coefficient (SHGC), emissivity, monthly average dry bulb temperature, monthly average percent humidity, monthly average wind speed, monthly average direct solar radiation, monthly average diffuse solar radiation, orientation, month as the input variables and saved heating or cooling loads as the output. The best architecture of 10-10-1 with mean absolute percentage error (MAPE), root mean square error (RMSE) and R2 results of 1.4%, 0.008 and 0.985 respectively showed the excellent predictiv..." @default.
• W2024351726 created "2016-06-24" @default.
• W2024351726 creator A5058375160 @default.
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• W2024351726 date "2019-08-23" @default.
• W2024351726 modified "2023-10-05" @default.
• W2024351726 title "Developing an empirical predictive saved load-rating model for windows by using artificial neural network" @default.
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• W2024351726 doi "https://doi.org/10.1080/15435075.2012.738451" @default.
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