Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments

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Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

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Mazlan Abdul Wahid, Syahrullail Samion, Aminuddin Saat, Nor Azwadi Che Sidik, Normah M. Ghazali, Nazri Kamsah, Azhar A. Aziz, Farid N. Ani, Haslinda M. Kamar and Mohsin Sies

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541-545

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Sholahudin et al., "Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments", Applied Mechanics and Materials, Vol. 819, pp. 541-545, 2016

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January 2016

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$38.00

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