Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool

Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user’s design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Naïve Bayes, Decision Tree, and k-Nearest Neighbor. Our experiments hows that Decision Tree has the fastest classification time followed by Naïve Bayes and k-Nearest Neighbor. The differences between classification time of Decision Tree and Naïve Bayes also between Naïve Bayes and k-NN are about an order of magnitude. Based on Percision, Recall, Fmeasure, Accuracy, and AUC, the performance of Naïve Bayes is the best. It outperforms Decision Tree and k-Nearest Neighbor on all parameters but precision. Keywords—energy simulation tool; classification method; naïve bayes; decision tree; k-nearest neighbor