Decision Tree Method for Energy Demand Modeling
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
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. 1637-
1646, Oct. 2010
[2] James, Witten, Hastie and Tibshirani. An
Introduction to Statistical Learning with
Applications in R. Springer Texts in Statistics
Vol. 103, 2013
2
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/read2
05630.html
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
http://www.taopic.com/vector/201212/286317.html
Cons & Pros
•
Comparison among three method in this paper
•
Decision tree wins!(the accuracies are almost
the same)
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]
Decision Tree Generation
•
Attribute Selection Criterion
S
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Results - The Decision Tree
8
Taken from [1]
●
Decision Tree from
training data
●
Confusion matrix for
training data using
decision tree
Results - Prediction on Test Data
9
Taken from [1]
Results - Nodes pt. 1
10
Taken from [1]
Non-Leaf Node:
●
Node #
●
# of Data Instances
●
Entropy Value
●
Split Attribute
Result - Nodes pt. 2
11
Taken from [1]
Leaf Node:
●
Node #
●
# of Data Instances
●
Avg. EUI
●
EUI Class
●
Stopping Criteria Met
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
Observations - Important Attributes
13
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
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
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This showcase presents a decision tree method developed by Zhun Yu, Fariborz Haghighat, Benjamin C.M. Fung, and Hiroshi Yoshino for building energy demand modeling at Worcester Polytechnic Institute. The method utilizes simple rules to partition variables and improve building design efficiency by predicting Energy Use Intensity (EUI). The comparison with other methods highlights decision tree's advantages in interpretability and execution ease. The project involved field surveys on energy-related data in residential buildings, and the process included data set collection, decision tree generation, and accuracy estimation.
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Presentation Transcript
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. 1637- 1646, Oct. 2010 [2] James, Witten, Hastie and Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer Texts in Statistics Vol. 103, 2013 Worcester Polytechnic Institute 2
Why Predicting EUI matters? EUI stands for Energy Use Intensity Energy consumption throughout the world increased significantly . For efficient building design Taken from baidu.com/imaghttp://news.zhulong.com/read2 05630.html Worcester Polytechnic Institute 3
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 http://www.taopic.com/vector/201212/286317.html Worcester Polytechnic Institute
Cons & Pros Comparison among three method in this paper Decision tree wins!(the accuracies are almost the same) Tree methods Regression ANN models can model complex relationships complicated to operate models simple and efficient Advantage understandable interpretable easy to execute too easy Disadvantage hard to interpret 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 Splitting dataset into training and test data Attribute Selection Criterion Generating decision tree using training data Estimating the accuracy Improve the model Worcester Polytechnic Institute
Results - The Decision Tree Decision Tree from training data Confusion matrix for training data using decision tree Taken from [1] Worcester Polytechnic Institute 8
Results - Prediction on Test Data Taken from [1] Worcester Polytechnic Institute 9
Results - Nodes pt. 1 Non-Leaf Node: Node # # of Data Instances Entropy Value Split Attribute Worcester Polytechnic Institute 10 Taken from [1]
Result - Nodes pt. 2 Leaf Node: Node # # of Data Instances Avg. EUI EUI Class Stopping Criteria Met Taken from [1] Worcester Polytechnic Institute 11
Results - Decision Rules 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 12
Observations - Important Attributes Worcester Polytechnic Institute 13
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 Worcester Polytechnic Institute 14
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 Worcester Polytechnic Institute 15
