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Development of enthalpy-based climate indicators for characterizing building cooling and heating energy demand under climate change

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  • Tian, Chuyin
  • Huang, Guohe
  • Lu, Chen
  • Zhou, Xiong
  • Duan, Ruixin

Abstract

The global warming trend is considered to significantly reshape the energy budgets of buildings in the future. Traditional characterization indices (i.e., HDDs and CDDs) for residential cooling- and heating-related energy demand neglect the variability in the latent heat of humid air. In this study, with the incorporation of more reliable climate information, high-resolution enthalpy-based climate indicators (HEDs and CEDs) are developed to better characterize climate change impacts on building energy demand. Compared with the proposed indicator, HDDs and CDDs will overestimate and underestimate the energy demand of buildings, respectively. This is especially the case for regions such as South Africa where apparent alternation of humid and dry seasons exists. A higher correlation of HEDs and CEDs with South African electricity consumption as compared with that for CDDs and HDDs further demonstrates the effectiveness of the proposed high-resolution climate indicators. HEDs and CEDs derived from the downscaled climate variables are projected under three scenarios combining SSPs and RCPs to the end of this century. Negligible increments and slight decrements in cooling- and heating-related energy demand are projected in the SSP1-2.6 scenario. Under the SSP2-4.5 scenario, South Africa is likely to experience huge reductions in heating energy demand and relatively lower increases in cooling energy requirement. For the SSP5-8.5 scenario, enormous increments and decrements of cooling- and heating-related energy demand are expected in most of the highly urbanized cities in South Africa. The developed indicators are expected to outperform HDDs and CDDs in other regions with apparent alterations of humid and dry seasons.

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  • Tian, Chuyin & Huang, Guohe & Lu, Chen & Zhou, Xiong & Duan, Ruixin, 2021. "Development of enthalpy-based climate indicators for characterizing building cooling and heating energy demand under climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
  • Handle: RePEc:eee:rensus:v:143:y:2021:i:c:s1364032121000940
    DOI: 10.1016/j.rser.2021.110799
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    1. Trotter, Ian M. & Bolkesjø, Torjus Folsland & Féres, José Gustavo & Hollanda, Lavinia, 2016. "Climate change and electricity demand in Brazil: A stochastic approach," Energy, Elsevier, vol. 102(C), pages 596-604.
    2. Mourshed, Monjur, 2011. "The impact of the projected changes in temperature on heating and cooling requirements in buildings in Dhaka, Bangladesh," Applied Energy, Elsevier, vol. 88(11), pages 3737-3746.
    3. Ye, Yuxiang & Koch, Steven F. & Zhang, Jiangfeng, 2018. "Determinants of household electricity consumption in South Africa," Energy Economics, Elsevier, vol. 75(C), pages 120-133.
    4. Fiorentini, Massimo & Tartarini, Federico & Ledo Gomis, Laia & Daly, Daniel & Cooper, Paul, 2019. "Development of an enthalpy-based index to assess climatic potential for ventilative cooling of buildings: An Australian example," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
    6. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    7. Zheng, Shuguang & Huang, Guohe & Zhou, Xiong & Zhu, Xiaohang, 2020. "Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 261(C).
    8. Yin, J.N. & Huang, G.H. & Xie, Y.L. & An, Y.K., 2021. "Carbon-subsidized inter-regional electric power system planning under cost-risk tradeoff and uncertainty: A case study of Inner Mongolia, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Wang, Yuan & Li, Li & Kubota, Jumpei & Han, Rong & Zhu, Xiaodong & Lu, Genfa, 2016. "Does urbanization lead to more carbon emission? Evidence from a panel of BRICS countries," Applied Energy, Elsevier, vol. 168(C), pages 375-380.
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