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Energy Efficiency Predicting using Artificial Neural Network

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dc.contributor.author Khalil, Ahmed J.
dc.contributor.author Barhoom, Alaa M.
dc.contributor.author Abu-Nasser, Bassem S.
dc.contributor.author Musleh, Musleh M.
dc.contributor.author Abu-Naser, Samy S.
dc.date.accessioned 2019-10-26T06:28:19Z
dc.date.available 2019-10-26T06:28:19Z
dc.date.issued 2019
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/487
dc.description.abstract Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as well as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. en_US
dc.language.iso en_US en_US
dc.subject Building, Energy en_US
dc.subject Prediction en_US
dc.subject Neural Networks en_US
dc.subject ANN en_US
dc.title Energy Efficiency Predicting using Artificial Neural Network en_US
dc.type Article en_US


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