A decision tree method for building energy demand modeling

Zhun Yu, Fariborz Haghighat, Benjamin C.M. Fung, Hiroshi Yoshino

Research output: Contribution to journalArticlepeer-review

374 Citations (Scopus)


This paper reports the development of a building energy demand predictive model based on the decision tree method. This method is able 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. 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. 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.

Original languageEnglish
Pages (from-to)1637-1646
Number of pages10
JournalEnergy and Buildings
Issue number10
Publication statusPublished - 2010 Oct


  • Building energy consumption
  • Classification analysis
  • Decision tree
  • Modeling

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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