Structural variation of cooling and heating energy demand with degree-day estimation method
9 affiliations
- 1CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing 401147, China; Geleshan National Climate Observatory, Chongqing 401147, China; Chongqing Climate Center, Chongqing 401147, China
- 2Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China; Institute of Advanced Interdisciplinary Studies, Chongqing University, Chongqing 400045, China; Corresponding author
- 3Department of Architecture, College of Design and Engineering, National University of Singapore, Singapore 117356, Singapore
- 4Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, China Three Gorges University, Yichang 443002, China
- 5Jangho Architecture College, Northeastern University, Shenyang 110169, China
- 6School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
- 7Tsinghua Shenzhen International Graduate School, Tsinghua University, Nanshan, Shenzhen 518071, China
- 8School of Architecture, Tsinghua University, Beijing 100084, China
- 9School of Resources and Environment, Nanchang University, Nanchang 330031, China; Leverhulme Centre for Anthropocene Biodiversity, University of York, York YO10 5DD, UK
Abstract
Degree-day methods are essential for estimating building heating and cooling energy demands. We investigate the sensitivity of cooling and heating degree-day models, CDD26 and HDD18, at national and regional scales across mainland China. Non-observational datasets overestimate national heating demand while severely underestimating cooling demand. Compared with current building codes, substituting estimation indicators and calculation models amplifies national cooling demand by 29.7%–100.6%, whereas national heating demand variations are −5.6% to 1.8%. Estimation methods prominently affect cooling demand in hot-summer zones, fundamentally reshaping the cooling energy structures of HSCWZ and CZ. While heating demand in warmer zones exhibits higher sensitivity to estimation indicators and calculation models, this sensitivity does not alter their overarching heating energy structures. Structurally, ASHRAE’s T avg formulation increases indicator variability, whereas UKMO mitigates this variability but systematically inflates CDD26 and HDD18. T avg benchmarking paired with asymmetric T max and T min dominant coefficients structurally control degree-day sensitivity across calculation models.
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BibTeX
@article{zhang2026structural,
title = {{Structural variation of cooling and heating energy demand with degree-day estimation method}},
author = {Tianyu Zhang and Bao-Jie He and Yonghua Li and Jinda Qi and Ke Xiong and Xin Dong and Jun Yang and Junqing Tang and Haoxiang Zhan and Pingan Ni and Guoen Wei},
journal = {Cell Reports Physical Science},
year = {2026},
month = oct,
doi = {10.1016/j.xcrp.2026.103611},
url = {https://doi.org/10.1016/j.xcrp.2026.103611},
note = {CC BY},
}
RIS
TY - JOUR
TI - Structural variation of cooling and heating energy demand with degree-day estimation method
AU - Tianyu Zhang
AU - Bao-Jie He
AU - Yonghua Li
AU - Jinda Qi
AU - Ke Xiong
AU - Xin Dong
AU - Jun Yang
AU - Junqing Tang
AU - Haoxiang Zhan
AU - Pingan Ni
AU - Guoen Wei
PY - 2026
DA - 2026/10/30
JO - Cell Reports Physical Science
DO - 10.1016/j.xcrp.2026.103611
UR - https://doi.org/10.1016/j.xcrp.2026.103611
AB - Degree-day methods are essential for estimating building heating and cooling energy demands. We investigate the sensitivity of cooling and heating degree-day models, CDD26 and HDD18, at national and regional scales across mainland China. Non-observational datasets overestimate national heating demand while severely underestimating cooling demand. Compared with current building codes, substituting estimation indicators and calculation models amplifies national cooling demand by 29.7%–100.6%, whereas national heating demand variations are −5.6% to 1.8%. Estimation methods prominently affect cooling demand in hot-summer zones, fundamentally reshaping the cooling energy structures of HSCWZ and CZ. While heating demand in warmer zones exhibits higher sensitivity to estimation indicators and calculation models, this sensitivity does not alter their overarching heating energy structures. Structurally, ASHRAE’s T avg formulation increases indicator variability, whereas UKMO mitigates this variability but systematically inflates CDD26 and HDD18. T avg benchmarking paired with asymmetric T max and T min dominant coefficients structurally control degree-day sensitivity across calculation models.
SN - 2666-3864
ER -