Machine learning approaches for estimating commercial building energy consumption

Caleb Robinson, Bistra Dilkina, Jeffrey Hubbs, Wenwen Zhang, Subhrajit Guhathakurta, Marilyn A. Brown, Ram M. Pendyala

Research output: Contribution to journalArticlepeer-review

322 Scopus citations


Building energy consumption makes up 40% of the total energy consumption in the United States. Given that energy consumption in buildings is influenced by aspects of urban form such as density and floor-area-ratios (FAR), understanding the distribution of energy intensities is critical for city planners. This paper presents a novel technique for estimating commercial building energy consumption from a small number of building features by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS). Our results show that gradient boosting regression models perform the best at predicting commercial building energy consumption, and can make predictions that are on average within a factor of 2 from the true energy consumption values (with an r2 score of 0.82). We validate our models using the New York City Local Law 84 energy consumption dataset, then apply them to the city of Atlanta to create aggregate energy consumption estimates. In general, the models developed only depend on five commonly accessible building and climate features, and can therefore be applied to diverse metropolitan areas in the United States and to other countries through replication of our methodology.

Original languageEnglish (US)
Pages (from-to)889-904
Number of pages16
JournalApplied Energy
StatePublished - Dec 15 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • General Energy
  • Management, Monitoring, Policy and Law
  • Building and Construction
  • Renewable Energy, Sustainability and the Environment


  • Commercial building energy consumption
  • Machine learning
  • Modeling


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