Worcester Polytechnic Institute CS548 Spring 2016 Decision Trees Showcase By Yi Jiang and Brandon Boos ---- Showcase work by Zhun Yu, Fariborz Haghighat,

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Worcester Polytechnic Institute CS548 Spring 2016 Decision Trees Showcase By Yi Jiang and Brandon Boos ---- Showcase work by Zhun Yu, Fariborz Haghighat, Benjamin C.M. Fung, and Hiroshi Yoshino on A decision tree method for building energy demand modeling

Worcester Polytechnic Institute References [1] Zhun Yu, Fariborz Haghighat, Benjamin C.M. Fung, and Hiroshi Yoshino. “A decision tree method for building energy demand modeling,” Energy and Building, Vol. 48, no. 10, pp , Oct [2] James, Witten, Hastie and Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer Texts in Statistics Vol. 103,

Worcester Polytechnic Institute Why Predicting EUI matters? EUI stands for Energy Use Intensity Energy consumption throughout the world increased significantly. For efficient building design 3 Taken from baidu.com/imaghttp://news.zhulong.com/read html

Worcester Polytechnic Institute Overview of Decision tree What is a tree? A tree is a prediction method with simple rules to divide the range of variables into smaller and smaller sections. Taken from

Worcester Polytechnic Institute Cons & Pros Comparison among three method in this paper Decision tree wins!(the accuracies are almost the same) Tree methodsRegression models ANN models Advantage understandable interpretable easy to execute simple and efficient can model complex relationships Disadvantage too easyhard to interpret complicated to operate

Worcester Polytechnic Institute Data Set In this project, field surveys on energy related data and other relevant information were carried out in 80 residential buildings in six different districts in Japan 13 observations have missing value. They use 55 observations in training data set Target variable:EUI: high or low Variables: Taken from [1]

Worcester Polytechnic Institute Decision Tree Generation Attribute Selection Criterion Splitting dataset into training and test data Generating decision tree using training data Estimating the accuracy Improve the model

Worcester Polytechnic Institute Results - The Decision Tree 8 Taken from [1] ●Decision Tree from training data ●Confusion matrix for training data using decision tree

Worcester Polytechnic Institute Results - Prediction on Test Data 9 Taken from [1]

Worcester Polytechnic Institute Results - Nodes pt Taken from [1] Non-Leaf Node: ●Node # ●# of Data Instances ●Entropy Value ●Split Attribute

Worcester Polytechnic Institute Result - Nodes pt Taken from [1] Leaf Node: ●Node # ●# of Data Instances ●Avg. EUI ●EUI Class ●Stopping Criteria Met

Worcester Polytechnic Institute Results - Decision Rules 12 Example Rule for Node 10: If TEMP is high and HLC < 3.89 and ELA < 4.41 and HWS is electric then EUI is LOW Taken from [1] and modified

Worcester Polytechnic Institute Observations - Important Attributes 13

Worcester Polytechnic Institute Observations - Interesting The importance of attributes for high and low temperature areas are different High temperature areas benefit from a certain value of equivalent leakage area as long as the heat loss coefficient is low enough 14

Worcester Polytechnic Institute Conclusions The decision tree provides an easily understood model which can help building designers and owners know which attributes to prioritize in order to lower energy use Non-binary classification could improve the results but would also increase chance of misclassification Larger data set needed 15