Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines

• We propose EMARS by fusing multivariate adaptive regression splines (MARS) and artificial bee colony (ABC).
• We investigate EMARS performance in predicting cooling load (CL) and heating load (HL) of buildings.
• We use EMARS to find mapping functions of HL and CL with their input variables.
• Surface area and roof area are identified as the critical factors influencing HL of buildings.
• Statistical results found EMARS to provide significant prediction accuracy compared to other methods.

This paper proposes using evolutionary multivariate adaptive regression splines (EMARS), an artificial intelligence (AI) model, to efficiently predict the energy performance of buildings (EPB). EMARS is a hybrid of multivariate adaptive regression splines (MARS) and artificial bee colony (ABC). In EMARS, MARS addresses learning and curve fitting and ABC carries out optimization to determine the fittest parameter settings with minimal prediction error. The proposed model was constructed using 768 experimental datasets from the literature, with eight input parameters and two output parameters (cooling load (CL) and heating load (HL)). EMARS performance was compared against five other AI models, including MARS, back-propagation neural network (BPNN), radial basis function neural network (RBFNN), classification and regression tree (CART), and support vector machine (SVM). A 10-fold cross-validation approach found EMARS to be the best model for predicting CL and HL with 65% and 45% deduction in terms of RMSE, respectively, compared to other methods. Furthermore, EMARS is able to operate autonomously without human intervention or domain knowledge; represent derived relationship between response (HL and CL) with predictor variables associated with their relative importance.

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