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Research Article

The energy performance and passive survivability of high thermal insulation buildings in future climate scenarios

Ran Wang1,2, Shilei Lu1,2( ), Xue Zhai1,2, Wei Feng3
School of Environment Science and Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
Tianjin Key Laboratory of Built Environment and Energy Application, Tianjin University, Tianjin, 300350, China
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Abstract

Given that the passive performance simulation of buildings based on typical meteorological year data and specific design schemes makes it challenging to respond to climate change and refine design requirements on time, this article established a passive performance prediction model for future buildings considering multi-dimensional variables including climate change, building design, and operational characteristics. For high thermal insulation buildings under future climates, the mild climate zone is more sensitive than the others, cooling energy demand is more sensitive than heating demand, apartments are more sensitive than office buildings, and passive survivability is more sensitive than energy performance; for buildings of the same type located in the same climate zone, thermal design solutions determine the increase rate of cooling demand. The potential benefits of climate warming on heating demand reduction are almost zero, but the cooling demand increases significantly, with apartments and office buildings increasing up to 22.1% and 5.0%, respectively. Buildings generally overheat in the future, and the increase rate of the mild zone far exceeds other zones with duration and severity being 3004.8% and 877.7% for apartments, and 884.3% and 288.9% for office buildings, respectively.

Keywords

climate changeheating demandcooling demandthermal insulationoverheating riskscenarios

References

 

Aste N, Angelotti A, Buzzetti M (2009). The influence of the external walls thermal inertia on the energy performance of well insulated buildings. Energy and Buildings, 41: 1181–1187.
Crossref Google Scholar
 

Baniassadi A, Heusinger J, Sailor DJ (2018). Energy efficiency vs resiliency to extreme heat and power outages: The role of evolving building energy codes. Building and Environment, 139: 86–94.
Crossref Google Scholar
 

Baniassadi A, Sailor DJ (2018). Synergies and trade-offs between energy efficiency and resiliency to extreme heat—A case study. Building and Environment, 132: 263–272.
Crossref Google Scholar
 

Baniassadi A, Sailor DJ, Krayenhoff ES, et al. (2019). Passive survivability of buildings under changing urban climates across eight US cities. Environmental Research Letters, 14: 074028.
Crossref Google Scholar
 

Belcher SE, Hacker JN, Powell DS (2005). Constructing design weather data for future climates. Building Services Engineering Research and Technology, 26: 49–61.
Crossref Google Scholar
 

Berardi U, Jafarpur P (2020). Assessing the impact of climate change on building heating and cooling energy demand in Canada. Renewable and Sustainable Energy Reviews, 121: 109681.
Crossref Google Scholar
 

Carlucci S, Bai L, de Dear R, et al. (2018). Review of adaptive thermal comfort models in built environmental regulatory documents. Building and Environment, 137: 73–89.
Crossref Google Scholar
 

Chai J, Huang P, Sun Y (2019). Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions. Energy, 185: 176–189.
Crossref Google Scholar
 

Chen X, Yang H (2018). Integrated energy performance optimization of a passively designed high-rise residential building in different climatic zones of China. Applied Energy, 215: 145–158.
Crossref Google Scholar
 

Chow DHC, Li Z, Darkwa J (2013). The effectiveness of retrofitting existing public buildings in face of future climate change in the hot summer cold winter region of China. Energy and Buildings, 57: 176–186.
Crossref Google Scholar
 

Crawley DB, Lawrie LK, Winkelmann FC, et al. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.
Crossref Google Scholar
 

de Dear R, Xiong J, Kim J, et al. (2020). A review of adaptive thermal comfort research since 1998. Energy and Buildings, 214: 109893.
Crossref Google Scholar
 

Dino IG, Akgül CM (2019). Impact of climate change on the existing residential building stock in Turkey: An analysis on energy use, greenhouse gas emissions and occupant comfort. Renewable Energy, 141: 828–846.
Crossref Google Scholar
 
DOE (2018). Commercial Prototype Building Models. Available at https://www.energycodes.gov/development/commercial/prototype_models.
 
DOE (2020). Weather Data. EnergyPlus. https://www.energyplus.net/weather.
 

Gang W, Wang S, Xiao F, et al. (2015). Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability. Applied Energy, 159: 265–275.
Crossref Google Scholar
 

Gou S, Nik VM, Scartezzini JL, et al. (2018). Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy and Buildings, 169: 484–506.
Crossref Google Scholar
 

Gupta R, Gregg M (2018). Assessing energy use and overheating risk in net zero energy dwellings in UK. Energy and Buildings, 158: 897–905.
Crossref Google Scholar
 

Hamdy M, Carlucci S, Hoes PJ, et al. (2017). The impact of climate change on the overheating risk in dwellings—A Dutch case study. Building and Environment, 122: 307–323.
Crossref Google Scholar
 

Harkouss F, Fardoun F, Biwole PH (2018). Passive design optimization of low energy buildings in different climates. Energy, 165: 591–613.
Crossref Google Scholar
 

Heracleous C, Michael A (2018). Assessment of overheating risk and the impact of natural ventilation in educational buildings of Southern Europe under current and future climatic conditions. Energy, 165: 1228–1239.
Crossref Google Scholar
 

Hopfe CJ, Hensen JLM (2011). Uncertainty analysis in building performance simulation for design support. Energy and Buildings, 43: 2798–2805.
Crossref Google Scholar
 

Huang P, Huang G, Wang Y (2015). HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique. Energy and Buildings, 91: 26–36.
Crossref Google Scholar
 
IPCC (2001). Data: Simulations, TAR. SRES scenarios, HadCM3 Climate Scenario Data. Intergovernmental Panel on Climate Change. Available at https://ipcc-data.org/sim/gcm_clim/SRES_TAR/index.html.
 

Levy S, Steinberg DM (2010). Computer experiments: A review. AStA Advances in Statistical Analysis, 94: 311–324.
Crossref Google Scholar
 

Liu S, Kwok YT, Lau KKL, et al. (2020). Development and application of future design weather data for evaluating the building thermal-energy performance in subtropical Hong Kong. Energy and Buildings, 209: 109696.
Crossref Google Scholar
 
MOHURD (2015). GB50189. Energy-Saving Design Standards for Public Buildings. Ministry of Housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2016). GB50176-2016. Civil Building Thermal Design Standard. Ministry of Housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2019). GB/T 51350-2019. Technical Standard for Nearly Zero Energy Buildings. Ministry of Housing and Urban-Rural Development of China. (in Chinese)
 

Moazami A, Carlucci S, Nik VM, et al. (2019). Towards climate robust buildings: An innovative method for designing buildings with robust energy performance under climate change. Energy and Buildings, 202: 109378.
Crossref Google Scholar
 

Pajek L, Košir M (2018). Implications of present and upcoming changes in bioclimatic potential for energy performance of residential buildings. Building and Environment, 127: 157–172.
Crossref Google Scholar
 

Rodrigues E, Fernandes MS (2020). Overheating risk in Mediterranean residential buildings: Comparison of current and future climate scenarios. Applied Energy, 259: 114110.
Crossref Google Scholar
 
Samuelson H, Goyal A, Claussnitzer S, et al. (2015). Toward pre- simulated guidelines for low-energy high-rise residential design in megacities. In: Proceedings of the Symposium on Simulation for Architecture & Urban Design, Alexandria, VA, USA.
 

Santamouris M (2014). On the energy impact of urban heat island and global warming on buildings. Energy and Buildings, 82: 100–113.
Crossref Google Scholar
 

Santamouris M, Paolini R, Haddad S, et al. (2020). Heat mitigation technologies can improve sustainability in cities. An holistic experimental and numerical impact assessment of urban overheating and related heat mitigation strategies on energy consumption, indoor comfort, vulnerability and heat-related mortality and morbidity in cities. Energy and Buildings, 217: 110002.
Crossref Google Scholar
 

Shen P (2017). Impacts of climate change on US building energy use by using downscaled hourly future weather data. Energy and Buildings, 134: 61–70.
Crossref Google Scholar
 
Sustainable Energy Research Group (2009). Climate Change World Weather File Generator for World-Wide Weather Data- CCWorldWeatherGen. University of Southampton, Southampton, UK.
 

Tian W (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20: 411–419.
Crossref Google Scholar
 

Wang R, Lu S (2020). A novel method of building climate subdivision oriented by reducing building energy demand. Energy and Buildings, 216: 109999.
Crossref Google Scholar
 

Xu P, Huang YJ, Miller N, et al. (2012). Impacts of climate change on building heating and cooling energy patterns in California. Energy, 44: 792–804.
Crossref Google Scholar
 

Yang L, Lyu K, Li H, et al. (2020). Building climate zoning in China using supervised classification-based machine learning. Building and Environment, 171: 106663.
Crossref Google Scholar
Building Simulation
Volume 15 Issue 7,
July 2022
Pages 1209-1225
DOI: 10.1007/s12273-021-0818-3
Cite this article:
Wang R, Lu S, Zhai X, et al. The energy performance and passive survivability of high thermal insulation buildings in future climate scenarios. Building Simulation, 2022, 15(7): 1209-1225. https://doi.org/10.1007/s12273-021-0818-3

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Received: 08 January 2021
Revised: 22 June 2021
Accepted: 25 June 2021
Published: 21 August 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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