Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines

Min Yuan Cheng, Minh Tu Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

168 Scopus citations


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.

Original languageEnglish
Pages (from-to)178-188
Number of pages11
JournalApplied Soft Computing Journal
StatePublished - Sep 2014


  • Artificial bee colony
  • Artificial intelligence
  • Cooling load
  • Energy performance of buildings
  • Heating load
  • Multivariate adaptive regression splines


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