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The energy performance of buildings was estimated using various data mining techniques, including support vector regression (SVR), artificial neural network (ANN), classification and regression tree, chi-squared automatic interaction detector, general linear regression, and ensemble inference model. The prediction models were constructed using 768 experimental datasets from the literature with 8 input parameters and 2 output parameters (cooling load (CL) and heating load (HL)). Comparison results showed that the ensemble approach (SVR +ANN) and SVR were the best models for predicting CL and HL, respectively, with mean absolute percentage errors below 4%. Compared to previous works, the ensemble model and SVR model further obtained at least 39.0% to 65.9% lower root mean square errors, respectively, for CL and HL prediction. This study confirms the efficiency, effectiveness, and accuracy of …