PREDICTION AND ANALYSIS OF BUILDING ENERGY EFFICIENCY USING ARTIFICIAL NEURAL NETWORKS AND DESIGN OF EXPERIMENTS
Keywords:
Building, Energy, Prediction, Neural Networks, Design of ExperimentAbstract
In this paper, an artificial neural network model has been developed to predict the heating and cooling loads of a building based on simulation data for building energy performance. The input variables include the overall height, relative compactness, surface area, wall area, roof area, orientation, glazing area, and glazing area distribution of building. The output variables are the heating and cooling loads. The simulation data used for training the model are the data published in the literature for various parametric combinations of a residential building. Artificial neural networks (ANNs) have a merit in estimating output values for given input values satisfactorily, but they have a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the individual variables, we used a method for the design of experiment (DOE) and conducted an analysis of variance (ANOVA). As the result, overall height, relative compactness, wall area, and glazing area have significant effect to reduce heating and cooling loads. Moreover, the surface area is influential on the heating load and the roof area in regard to the cooling load only.References
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