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This paper describes the use of decision tree method to predict thermal performance of several roof systems under different climate conditions. The decision tree method is a data mining technique which has competitive advantages over other methods such as simple and clear procedure, easy to understand without having rigorous mathematical and computational knowledge, etc. Results of 80energy simulation cases were used to demonstrate the applicability of this method in building energy simulation. These 80 simulation cases are based on five locations in five different climate zones, eight different roof systems, and two extreme climate conditions; warmest and coldest in a year of a particular location. The modelled decision tree has prediction accuracy of 84% on training data and 100% on test data. Addition to that, decision tree automatically ranked the best selection of roof system under prevailing climate conditions. The predicted values shown in each classified data subsets can be used as a reference with an accuracy of 6%to predict the indoor room temperature with the use of a particular roof system. Finally, derived decision rules and simplified guidelines from constructed decision tree are also provided in a tabular format for non-engineer users. ENGINEER, Vol. 47, No. 03, pp. 27-37, 2014

Predicting Thermal Performance of Different Roof Systems by Using Decision Tree Method