Abstract
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
Highlights
The significant percentage of total energy consumption of a building is used to restore the acceptable occupant thermal comfort level [1, 2]
The traditional regression analysis method and Artificial Neural Network (ANN) method are two of the most popular techniques successfully used by researchers in the past [3]
Yu et al.[18] demonstrated the use of decision tree method in building energy simulation in detail analysis by predicting energy use intensity of houses in Japan.Tso and Yau [19] compared the accuracy of the decision tree method with regression analysis and ANN method and found that its accuracy was almost the same as other two methods
Summary
The significant percentage of total energy consumption of a building is used to restore the acceptable occupant thermal comfort level [1, 2]. The direct and indirect heat transfer of building components are the main factors affecting the occupant thermal comfort by increasing the indoor temperature. Energy simulation techniques have been widely used to assess the thermal performance of the whole or part of a building [3, 4]. The traditional regression analysis method and Artificial Neural Network (ANN) method are two of the most popular techniques successfully used by researchers in the past [3]. The simple and efficient regression analysis method is based on statistical analysis and regression equation which is able to combine effect of various climate variables with building physics in order to predict building energy demand [5, 6, 7]. The use of the decision tree method in building energy simulation is very sparse. Yu et al.[18] demonstrated the use of decision tree method in building energy simulation in detail analysis by predicting energy use intensity of houses in Japan.Tso and Yau [19] compared the accuracy of the decision tree method with regression analysis and ANN method and found that its accuracy was almost the same as other two methods
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