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Predicting the response of heating and cooling demands of residential buildings with various thermal performances in China to climate change

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  • Xiong, Jie
  • Guo, Siyue
  • Wu, Yi
  • Yan, Da
  • Xiao, Chan
  • Lu, Xi

Abstract

Climate change will continuously impact building heating and cooling demands. Therefore, it is essential to characterise the varying future heating and cooling demands, which will help energy planning and avoid the lock-in effect of infrastructure. In this study, future meteorological data for building simulations were generated using global climate models under different shared socioeconomic pathways and were used in a large number of simulations by DeST to see the impacts on heating and cooling loads with different building performance and occupancy modes. Harbin, Beijing, Chongqing, Guangzhou, and Kunming were selected to exemplify the impacts on different climate zones. The future heating demand will decline evidently where the heating demand dominates, like Harbin, Beijing and Chongqing. For areas with a predominant cooling demand, like Guangzhou and Chongqing, the future cooling demand will increase significantly. As for enhanced building thermal performance, the annual heating and cooling demand are both effectively reduced, and the impacts of climate change are gradually diminishing. It is estimated that by 2060, the total energy consumption in urban residential buildings will be 5% higher considering the effect of climate change, and the percentage of cooling energy use will increase from 14% to 20%, resulting in higher importance of cooling energy sufficiency.

Suggested Citation

  • Xiong, Jie & Guo, Siyue & Wu, Yi & Yan, Da & Xiao, Chan & Lu, Xi, 2023. "Predicting the response of heating and cooling demands of residential buildings with various thermal performances in China to climate change," Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s0360544223001834
    DOI: 10.1016/j.energy.2023.126789
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    References listed on IDEAS

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    5. Khajavi, Hamed & Rastgoo, Amir, 2023. "Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms," Energy, Elsevier, vol. 272(C).

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