Skip to main content
Log in

The typical hot year and typical cold year for modeling extreme events impacts on indoor environment: A generation method and case study

  • Research Article
  • Building Thermal, Lighting, and Acoustics Modeling
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

Increasing climate change brings not only global warming, but also more frequent and severe extreme events, which greatly affect the indoor environment. There has been some research discussing the impacts of extreme events on indoor thermal environment, mostly related to heatwaves, but quite limited on extremely cold events which occurred in recent years. In this study, the indoor environment was simulated in a typical residential building in China’s hot summer and cold winter climate zone, that has faced both kinds of events, using ERA5 database (reanalysis weather data) in the past 40 years. The simulation results were analyzed for a better understanding of the indoor events of extreme hot or cold conditions and were used to select the weather data for modelling extreme event impacts, including a typical hot year and typical cold year defined in this study. The impacts of the indicator were used to evaluate overheat or extreme cold conditions, and the building envelope level were also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Al-Musaylh MS, Deo RC, Adamowski JF, Li Y (2019). Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia. Renewable and Sustainable Energy Reviews, 113: 109293.

    Article  Google Scholar 

  • An J, Yan D, Hong T, Sun K (2017). A novel stochastic modeling method to simulate cooling loads in residential districts. Applied Energy, 206: 134–149.

    Article  Google Scholar 

  • Bai L, Wang S (2019). Definition of new thermal climate zones for building energy efficiency response to the climate change during the past decades in China. Energy, 170: 709–719.

    Article  Google Scholar 

  • Cao J, Li M, Wang M, Xiong M, Meng F (2017). Effects of climate change on outdoor meteorological parameters for building energy-saving design in the different climate zones of China. Energy and Buildings, 146: 65–72.

    Article  Google Scholar 

  • Chen Y, Li Y (2017). An inter-comparison of three heat wave types in China during 1961–2010: observed basic features and linear trends. Scientific Reports, 7: 45619.

    Article  Google Scholar 

  • CMA (2011). The definition of heat waves. China Meteorological Administration. Available at http://www.cma.gov.cn/2011qxfw/2011qqxkp/2011qkpdt/201110/t20111026_124192.html. (in Chinese)

  • Ding T, Ke Z (2015). Characteristics and changes of regional wet and dry heat wave events in China during 1960–2013. Theoretical and Applied Climatology, 122: 651–665.

    Article  Google Scholar 

  • ECMWF (2019). Introduction of ERA5 dataset. European Centre for Medium-Range Weather Forecasts. Available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5

  • Feng X, Yan D, Hong T (2015). Simulation of occupancy in buildings. Energy and Buildings, 87: 348–359.

    Article  Google Scholar 

  • Guo S, Yan D, Cui Y (2014). Analysis on the influence of occupant behavior patterns to building envelope’s performance on space heating in residential buildings in Shanghai. In: Proceedings of the 2nd Asia IBPSA Conference, Nagoya, Japan.

  • Guo S, Yan D, Peng C, Cui Y, Zhou X, Hu S (2015). Investigation and analyses of residential heating in the HSCW climate zone of China: Status quo and key features. Building and Environment, 94: 532–542.

    Article  Google Scholar 

  • Guo S, Yan D, Hong T, Xiao C, Cui Y (2019). A novel approach for selecting typical hot-year (THY) weather data. Applied Energy, 242: 1634–1648.

    Article  Google Scholar 

  • Herrera M, Natarajan S, Coley DA, Kershaw T, Ramallo-González AP, Eames M, Fosas D, Wood M (2017). A review of current and future weather data for building simulation. Building Services Engineering Research and Technology, 38: 602–627.

    Article  Google Scholar 

  • Herrera M, Ramallo-González AP, Eames M, Ferreira AA, Coley DA (2018). Creating extreme weather time series through a quantile regression ensemble. Environmental Modelling & Software, 110: 28–37.

    Article  Google Scholar 

  • Hersbach H, Bell B, Berrisford P, Horányi A, Sabater JM, Nicolasm J, Radu, R, Schepers, D, Simmons, A, Soci C, Dee D (2019). Global reanalysis: goodbye ERA-Interim, hello ERA5. ECMWF Newsletter. No. 159.

  • IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Katal A, Mortezazadeh M, Wang LL (2019). Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations. Applied Energy, 250: 1402–1417.

    Article  Google Scholar 

  • Li DHW, Wan KKW, Yang L, Lam JC (2011). Heat and cold stresses in different climate zones across China: a comparison between the 20th and 21st centuries. Building and Environment, 46: 1649–1656.

    Article  Google Scholar 

  • Li Y, Li X (2019). Preliminary study on heating energy consumption distribution of dwellings in hot summer and cold winter climate region of China. Indoor and Built Environment, 28: 950–963.

    Article  Google Scholar 

  • Liang Z, Tian Z, Sun L, Feng K, Zhong H, Gu T, Liu X (2016). Heat wave, electricity rationing, and trade-offs between environmental gains and economic losses: The example of Shanghai. Applied Energy, 184: 951–959.

    Article  Google Scholar 

  • Liao Z, Gao M, Sun J, Fan S (2017). The impact of synoptic circulation on air quality and pollution-related human health in the Yangtze River Delta region. Science of the Total Environment, 607–608: 838–846.

    Article  Google Scholar 

  • Meng F, Li M, Cao J, Li J, Xiong M, Feng X, Ren G (2018). The effects of climate change on heating energy consumption of office buildings in different climate zones in China. Theoretical and Applied Climatology, 133: 521–530.

    Article  Google Scholar 

  • MOHURD (2010). Design standard for energy efficiency of residential buildings in hot summer and cold sinter zone (JGJ 134–2010). Ministry of Housing and Urban-Rural Development. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • MOHURD (2016). Code for thermal design of civil building (GB 50176–2016). Ministry of Housing and Urban-Rural Development Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • Nateghi R, Mukherjee S (2017). A multi-paradigm framework to assess the impacts of climate change on end-use energy demand. PLoS One, 12: e0188033.

    Article  Google Scholar 

  • Nik VM (2017). Application of typical and extreme weather data sets in the hygrothermal simulation of building components for future climate—A case study for a wooden frame wall. Energy and Buildings, 154: 30–45.

    Article  Google Scholar 

  • Pernigotto G, Prada A, Gasparella A (2020). Extreme reference years for building energy performance simulation. Journal of Building Performance Simulation, 13: 152–166.

    Article  Google Scholar 

  • Ragatoa DS, Ogunjobi KO, Klutse NAB, Okhimamhe AA, Eichie JO (2019). A change comparison of heat wave aspects in climatic zones of Nigeria. Environmental Earth Sciences, 78: 111.

    Article  Google Scholar 

  • Sakka A, Santamouris M, Livada I, Nicol F, Wilson M (2012). On the thermal performance of low income housing during heat waves. Energy and Buildings, 49: 69–77.

    Article  Google Scholar 

  • Shanghai Meteorological Service (2019). New kind of warning signal for low outdoor temperature is coming. Available at http://sh.cma.gov.cn/sh/news/qxyw/201905/t20190531_607246.html. (in Chinese)

  • Song F, Zhu Q, Wu R, Jiang Y, Xiong A, Wang B, Zhu Y, Li Q (2007). Meteorological data set for building thermal environment analysis of China. In: Proceedings of the 10th International Building Performance Simulation Association Conference and Exhibition, Beijing, China.

  • Spinoni J, Vogt JV, Barbosa P, Dosio A, McCormick N, Bigano A, Füssel HM (2018). Changes of heating and cooling degree-days in Europe from 1981 to 2100. International Journal of Climatology, 38: e191–e208. DOI: https://doi.org/10.1002/joc.5362.

    Article  Google Scholar 

  • THUBERC (2016). Report on nationwide survey on building performance, energy consumption and occupancy behavior. Beijing: Building Energy Research Center of Tsinghua University. (in Chinese)

    Google Scholar 

  • THUBERC (2017). China Building Energy Use 2017. Building Energy Research Center of Tsinghua University. Beijing, China: China Architecture & Building Press.

    Google Scholar 

  • Thomson H, Simcock N, Bouzarovski S, Petrova S (2019). Energy poverty and indoor cooling: an overlooked issue in Europe. Energy and Buildings, 196: 21–29.

    Article  Google Scholar 

  • Vine E (2012). Adaptation of California’s electricity sector to climate change. Climatic Change, 111: 75–99.

    Article  Google Scholar 

  • Wallace LA, Emmerich SJ, Howard-Reed C (2002). Continuous measurements of air change rates in an occupied house for 1 year: The effect of temperature, wind, fans, and windows. Journal of Exposure Science & Environmental Epidemiology, 12: 296–306.

    Article  Google Scholar 

  • Wan KKW, Li DHW, Pan W, Lam JC (2012). Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications. Applied Energy, 97: 274–282.

    Article  Google Scholar 

  • Wang P, Tang J, Sun X, Wang S, Wu J, Dong X, Fang J (2017). Heat waves in China: definitions, leading patterns, and connections to large-scale atmospheric circulation and SSTs. Journal of Geophysical Research: Atmospheres, 122: 10679–10699.

    Google Scholar 

  • Wang Z, de Dear R, Luo M, Lin B, He Y, Ghahramani A, Zhu Y (2018). Individual difference in thermal comfort: A literature review. Building and Environment, 138: 181–193.

    Article  Google Scholar 

  • WMO (2015). Heatwaves and Health: Guidance on Warning-System Development. Geneva: World Meteorological Organization.

    Google Scholar 

  • WMO (2019a). WMO Statement on the State of The Global Climate in 2018. Geneva: World Meteorological Organization.

    Google Scholar 

  • WMO (2019b). The Global Climate in 2015–2019. Geneva: World Meteorological Organization.

    Google Scholar 

  • Yan D, Xia J, Tang W, Song F, Zhang X, Jiang Y (2008). DeST—An integrated building simulation toolkit Part I: Fundamentals. Building Simulation, 1: 95–110.

    Article  Google Scholar 

  • Yang K, Zhang J, Wu L, Wei J (2019). Prediction of summer hot extremes over the middle and lower reaches of the Yangtze River valley. Climate Dynamics, 52: 2943–2957.

    Article  Google Scholar 

  • Zhang N, Cao B, Wang Z, Zhu Y, Lin B (2017). A comparison of winter indoor thermal environment and thermal comfort between regions in Europe, North America, and Asia. Building and Environment, 117: 208–217.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by “the 13th Five-Year” National Key R&D Program of China (No. 2018YFC0704504), the National Natural Science Foundation of China (No. 51908312), and the Innovative Research Groups of the National Natural Science Foundation of China (No. 51521005).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Siyue Guo or Da Yan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, S., Yan, D. & Gui, C. The typical hot year and typical cold year for modeling extreme events impacts on indoor environment: A generation method and case study. Build. Simul. 13, 543–558 (2020). https://doi.org/10.1007/s12273-020-0617-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-020-0617-2

Keywords

Navigation