This paper reports the development of a building energy demand predictive model based on the decision tree method. The developed model estimates the building energy performance indexes in a rapid and easy way. This method is appropriate to classify and predict categorical variables: its competitive advantage over other widely used modeling techniques, such as regression method and ANN method, lies in the ability to generate accurate predictive models with interpretable flowchart-like tree structures that enable users to quickly extract useful information. To demonstrate its applicability, the method is applied to estimate residential building energy performance indexes by modeling building energy use intensity (EUI) levels (either high or low). The results demonstrate that the use of decision tree method can classify and predict building energy demand levels accurately (93% for training data and 92% for test data), identify and rank significant factors of building EUI automatically. The method can provide the combination of significant factors as well as the threshold values that will lead to high building energy performance. Moreover, the average EUI value of data records in each classified data subsets can be used for reference when performing prediction. The outcomes of this methodology could benefit architects, building designers and owners greatly in the building design and operation stage. One crucial benefit is improving building energy performance and reducing energy consumption. Another advantage of this methodology is that it can be utilized by users without requiring much computation knowledge. Energy consumption, modeling, decision tree, classification analysis. There has been a growing concern about the total building energy consumption which is a substantial user of energy worldwide. Further, with rising living standards, building energy consumption throughout the world has been significantly increased over the past few decades. For example, from 1994 to 2004, building energy consumption in Europe and North America has increased at a rate of 1.5% and 1.9% per annum, respectively [1] . Chinese building energy consumption has increased at more than 10% per annum for the past 20 years [2] . The high level of building energy consumption and the steady increase in building energy demand necessitate designing energy efficient buildings and improving its energy performance. In the practice of energy efficient building design, architects and building designers often need to identify which parameters will influence future building energy demand significantly. Furthermore, based on different combinations of these parameters as well as their values, architects and building designers usually expect to find a simple and reliable method to estimate building energy performance rapidly so that they can optimize their building design plans. Building energy simulation tools have been utilized to forecast and analyze building energy consumption and describe building energy use patterns, in order to benefit the design and operation of energy efficient buildings. In recent years, there have been many studies on building energy demand modeling and several methods were employed, such as traditional regression methods [3] [4] , artificial neural networks (ANN) methods [5] [6] [7] , and building simulation methods [8] [9] , etc. Through statistical methods and regression equations, regression models correlate building energy demand with relevant climatic variables and/or building physical variables in order to predict energy demand. The main advantage of regression models is that they are comparatively simple and efficient. The ANN model is also able to predict the thermal performance of building and its foundation is based on mimicking the structure and properties of biological neural networks. The greatest strength of ANN models in comparison with other models lies in their ability to model complex relationships between inputs and outputs. These two methods have been successfully applied to predict building energy demand. However, considering the regression models are normally complicated equations and ANN models operate like a "black box"; therefore, the models developed using these methods are not understandable and interpretable especially for common users without advanced mathematical knowledge. This makes it difficult to be a common predictive tool. Moreover, in these studies, the focuses have been mainly on the energy use prediction of existing buildings (e.g. predict hourly heating/cooling load for a certain type of building), whereas the energy use prediction of newly designed buildings, which is also very important for architects and building designers to make rational decisions at the early stage of design and operation, are seldom carried out. Building simulation allows the prediction of building energy performance under various conditions. However, this method does not perform well in predicting the energy use for occupied buildings as compare to non-occupied buildings due to the lack of sufficient knowledge about occupants' behavior. Additionally, the application
In REF a decision tree is used to model the real consumption of residential buildings in order to predict the energy use of newly designed buildings.
A decision tree method for building energy demand modeling
Energy and Buildings
Engineering