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Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA

Mohammadiziazi, Rezvan and Bilec, Melissa M. (2020) Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA. Buildings, 10 (8). p. 139. ISSN 2075-5309

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Given the urgency of climate change, development of fast and reliable methods is essential to understand urban building energy use in the sector that accounts for 40% of total energy use in USA. Although machine learning (ML) methods may offer promise and are less difficult to develop, discrepancy in methods, results, and recommendations have emerged that requires attention. Existing research also shows inconsistencies related to integrating climate change models into energy modeling. To address these challenges, four models: random forest (RF), extreme gradient boosting (XGBoost), single regression tree, and multiple linear regression (MLR), were developed using the Commercial Building Energy Consumption Survey dataset to predict energy use intensity (EUI) under projected heating and cooling degree days by the Intergovernmental Panel on Climate Change (IPCC) across the USA during the 21st century. The RF model provided better performance and reduced the mean absolute error by 4%, 11%, and 12% compared to XGBoost, single regression tree, and MLR, respectively. Moreover, using the RF model for climate change analysis showed that office buildings’ EUI will increase between 8.9% to 63.1% compared to 2012 baseline for different geographic regions between 2030 and 2080. One region is projected to experience an EUI reduction of almost 1.5%. Finally, good data enhance the predicting ability of ML therefore, comprehensive regional building datasets are crucial to assess counteraction of building energy use in the face of climate change at finer spatial scale.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Mohammadiziazi, Rezvanrezvanziazi@pitt.edurezvanziazi
Bilec, Melissa M.mbilec@pitt.edumbilec
Date: 4 August 2020
Date Type: Publication
Journal or Publication Title: Buildings
Volume: 10
Number: 8
Publisher: MDPI AG
Page Range: p. 139
DOI or Unique Handle: 10.3390/buildings10080139
Schools and Programs: Swanson School of Engineering > Civil and Environmental Engineering
Refereed: Yes
Uncontrolled Keywords: machine learning, building energy use, climate change, prediction model, data-driven, random forest
ISSN: 2075-5309
Official URL:
Funders: National Science Foundation
Article Type: Research Article
Date Deposited: 04 Jun 2021 17:18
Last Modified: 04 Jun 2021 17:18


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