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Stochastic RCM-driven cooling and heating energy demand analysis for residential building

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  • Tian, Chuyin
  • Huang, Guohe
  • Piwowar, Joseph M.
  • Yeh, Shin-Cheng
  • Lu, Chen
  • Duan, Ruixin
  • Ren, Jiayan

Abstract

Changes associated with global warming are expected to significantly alter existing heating and cooling demands in buildings. In order to develop appropriate adaptation measures in response to these climate changes, policy makers will require reliable and comprehensive assessments of anticipated variations in building energy use. In this study, a stochastic RCM-driven residential energy demand analysis is developed to provide robust support for policy makers to consider various adaptation actions for building energy system. High-resolution climate projections are firstly generated via proposed approach, based on which the heating and cooling energy demand of residential buildings in the province of British Columbia, Canada is analyzed from a comprehensive perspective (i.e., considering optimistic, neutral, and pessimistic levels). The results show that energy-related infrastructure and adaptation policies are likely to face huge challenges in the next 80 years due to a large increase in the energy use demand for space cooling and a relatively slight decrease in that for heating. Under an optimistic level, annual heating energy use is projected to decline 6% while cooling energy use will increase by 23% in 2088–2099. However, under a pessimistic level these changes could surge by 21% and 88%, respectively. The difference in projected energy use between these two levels can be equivalent to 1.30 Mt of CO2e greenhouse gas emissions. The analysis proposed in this study could also be applied in other regions under similar contexts.

Suggested Citation

  • Tian, Chuyin & Huang, Guohe & Piwowar, Joseph M. & Yeh, Shin-Cheng & Lu, Chen & Duan, Ruixin & Ren, Jiayan, 2022. "Stochastic RCM-driven cooling and heating energy demand analysis for residential building," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:rensus:v:153:y:2022:i:c:s1364032121010340
    DOI: 10.1016/j.rser.2021.111764
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