Abstract
This paper proposes using evolutionary grey wolf algorithm to predict the heating load (HL) and the cooling load (CL) of buildings. The proposed algorithm was constructed using 768 various residential buildings with eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) and two output variables. The experimental results are evaluated by comparative to previous work, geometric semantic genetic programming (GSGP), artificial neural network (ANN), support vector regression (SVR), evolutionary multivariate adaptive regression splines (EMARS), random forests (RF) and multilayer perceptron (MLP). The results prove that the proposed algorithm is competitive compared to the other machine learning algorithms.
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