Quantifying the uncertain effects of climate change on building energy consumption across the United States

Jimeno A. Fonseca*, Ido Nevat, Gareth W. Peters

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)
55 Downloads (Pure)


Climate change could have both positive and negative effects on the energy consumption of buildings. Today, it is not clear what the extent of these effects could be at multiple spatial scales including building sectors, cities, and climate zones. More importantly, the uncertainty of mathematical models used to estimate these effects is not well understood. This knowledge gap makes it difficult to evaluate decisions about what buildings, cities, and even technologies should be prioritized in the race to mitigate climate change. Moreover, this lack of knowledge makes it difficult for researchers to build on the limitations of past models effectively. To address this knowledge gap, we develop a novel framework for quantifying model uncertainty in the context of climate change and building energy consumption. The framework blends for the first time large sources of weather and building energy consumption data with Bayesian statistics and first-principle building energy models. The framework is used to forecast the potential effects of climate change in buildings across 96 cities in the United States for the 21st century. Based on our estimates and credible intervals, we found reasons to support the idea that commercial buildings in hot/warm and humid climates should be at the top of the agenda of climate action in the building sector of the United States. We believe that future research on uncertainty quantification should take a closer look at the local effects of extreme events rather than yearly effects of climate change on buildings.

Original languageEnglish
Article number115556
JournalApplied Energy
Early online date14 Aug 2020
Publication statusPublished - 1 Nov 2020


  • Building energy demand
  • Climate change
  • Enthalpy gradients
  • Hierarchical Bayesian linear models
  • United States
  • Wide and deep neural networks

ASJC Scopus subject areas

  • Building and Construction
  • General Energy
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law


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