Articles | Volume 18, issue 10
https://doi.org/10.5194/essd-18-7417-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-7417-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
09 Oct 2026
Data description article |
| 09 Oct 2026
| 09 Oct 2026
A global base temperature dataset for residential building energy demand modelling
Xiujuan He
Department of Geography, The University of Hong Kong, Hong Kong, PR China
Jiyong Eom
School of Business & Technology Management, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
Sha Yu
Center for Global Sustainability, School of Public Policy, University of Maryland, College Park, MD, USA
Shu Liu
Department of Geography, The University of Hong Kong, Hong Kong, PR China
Wenru Xu
CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
Yuyu Zhou
CORRESPONDING AUTHOR
Department of Geography, The University of Hong Kong, Hong Kong, PR China
Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, PR China
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Short summary
Buildings use large amounts of energy for heating and cooling. Cutting carbon emissions requires accurate forecasts of that demand, which depends on knowing the outdoor temperature at which buildings switch on heating or cooling. We combined energy use records with climate information and applied machine learning to create the first global dataset of these thresholds across many regions. It improves energy demand predictions by about 10 %, supporting smarter energy planning and climate policy.
Buildings use large amounts of energy for heating and cooling. Cutting carbon emissions requires...
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