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Energy-efficient building design has become imperative for energy conservation, emissions reduction, and life quality enhancement of occupant. Physics-based whole building energy simulation is widely used to access building energy performance, which requires large amount of information to specify values of input parameters and includes underlying assumptions. This study proposed an alternative model based on machine learning (ML) to predict cooling loads of buildings with few common parameters in the design phase. The ML models were developed and evaluated using a dataset of 243 buildings. Predicted cooling loads from these models were compared to those from the physics-based whole building energy simulation. The proposed model exhibits good agreement with the physics-based whole building energy simulation. The analytical results present that the ML models obtained the correlation …