AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

Tradeoff between heating energy demand in winter and indoor overheating risk in summer constrained by building standards

Ran Wang1,2,3, Shilei Lu1,2,3( ), Wei Feng3, Bowen Xu1,2
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
Show Author Information

Abstract

Evidence indicates that improvement of thermal performance of building envelope has the potential for aggravating the indoor overheating risk in summer. On the other hand, evolving building standards continue to strengthen the requirements for thermal performance to achieve the energy-saving target. Therefore, this study quantifies the interaction effect between building standards-oriented building design, heating energy demand in winter, and indoor overheating risk in summer. Building databases with different energy efficiency levels are generated using a randomly generated method. Uncertain variables include not only 13 design parameters but also the running state of natural ventilation and external shading. The indoor overheating risk is assessed in terms of severity and duration. Finally, a multi-objective optimization model integrating meta-models and the non-dominated sorting genetic algorithm is proposed to balance heating energy demand in winter and indoor overheating risk in summer. Results indicate that building standards tend to aggravate overheating risk in summer: the duration and severity of high-performance buildings increased by 40.6% and 24.2% than that of conventional-performance buildings. However, window ventilation could offset the adverse effect, and mitigation of duration and severity can be up to 85.2% and 62.1% for high-performance buildings. Window ventilation can weaken the conflict between heating energy demand in winter and overheating risk in summer. As heating energy demand increased from 6.1 to 67.3 kWh/m2, the overheating risk changes little that the duration of overheating risk decreased from 17.5% to 15.6% and severity decreased from 8.7 °C to 8.3 °C.

Keywords

natural ventilationbuilding designclimate changeindoor overheating riskenergy codes

References

 
Ameur M, Kharbouch Y, Mimet A (2020). Optimization of passive design features for a naturally ventilated residential building according to the bioclimatic architecture concept and considering the northern Morocco climate. Building Simulation, 13: 677-689.
Crossref Google Scholar
 
ASHRAE (2002). ASHRAE Guideline 14-2002, Measurement of Energy and Demand Savings. Atlanta: American Society of Heating, Ventilating, Air Conditioning Engineers.
 
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, 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
 
Bayraktar NT, Ok V (2019). Numerical evaluation of the effects of different types of shading devices on interior occupant thermal comfort using wind tunnel experimental data. Building Simulation, 12: 683-696.
Crossref Google Scholar
 
Beizaee A, Lomas KJ, Firth SK (2013). National survey of summertime temperatures and overheating risk in English homes. Building and Environment, 65: 1-17.
Crossref Google Scholar
 
Bre F, Fachinotti VD (2017). A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings. Energy and Buildings, 154: 283-294.
Crossref Google Scholar
 
Candanedo LM, Feldheim V, Deramaix D (2017). Data driven prediction models of energy use of appliances in a low-energy house. Energy and Buildings, 140: 81-97.
Crossref Google Scholar
 
Carlucci S, Bai L, de Dear R, Yang L (2018). Review of adaptive thermal comfort models in built environmental regulatory documents. Building and Environment, 137: 73-89.
Crossref Google Scholar
 
Chen X, Yang H (2017). A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios. Applied Energy, 206: 541-557.
Crossref Google Scholar
 
CMA (2011). What is a Heat Wave? China Meteorological Administration. http://www.cma.gov.cn/2011qxfw/2011qqxkp/2011qkpdt/201110/t20111026_124192.html. (in Chinese)
 
Dengel A, Swainson M (2012). Overheating in new homes; A review of the evidence. Research Review, Zero Carbon Hub. NHBC Foundation
 
Derbez M, Berthineau B, Cochet V, Pignon C, Ribéron J, et al. (2014). A 3-year follow-up of indoor air quality and comfort in two energy-efficient houses. Building and Environment, 82: 288-299.
Crossref Google Scholar
 
DOE (2018). EnergyPlus™ Version 9.0.1 Documentation. Input Output Reference. U.S. Department of Energy.
 
Echenagucia TM, Capozzoli A, Cascone Y, Sassone M (2015). The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis. Applied Energy, 154: 577-591.
Crossref Google Scholar
 
Elsharkawy H, Zahiri S (2020). The significance of occupancy profiles in determining post retrofit indoor thermal comfort, overheating risk and building energy performance. Building and Environment, 172: 106676.
Crossref Google Scholar
 
Feng W, Zhang Q, Ji H, Wang R, Zhou N, et al. (2019). A review of net zero energy buildings in hot and humid climates: Experience learned from 34 case study buildings. Renewable & Sustainable Energy Reviews, 114: 109303.
Crossref Google Scholar
 
Fosas D, Coley DA, Natarajan S, Herrera M, de Pando MF, et al. (2018). Mitigation versus adaptation: Does insulating dwellings increase overheating risk? Building and Environment, 143: 740-759.
Crossref Google Scholar
 
Gou S, Nik VM, Scartezzini J-L, Zhao Q, Li Z (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
 
Guo S, Yan D, Gui C (2020). The typical hot year and typical cold year for modeling extreme events impacts on indoor environment: A generation method and case study. Building Simulation, 13: 543-558.
Crossref Google Scholar
 
Hatvani-Kovacs G, Belusko M, Pockett J, Boland J (2018). Heat stress-resistant building design in the Australian context. Energy and Buildings, 158: 290-299.
Crossref Google Scholar
 
IEA (2019). 2019 Global Status Report for Buildings and Construction Sector. https://www.unenvironment.org/resources/publication/2019-global-status-report-buildings-and-construction-sector. Accessed Jul 16 2020.
 
Jenkins DP, Peacock AD, Banfill PFG (2009). Will future low-carbon schools in the UK have an overheating problem? Building and Environment, 44: 490-501.
Crossref Google Scholar
 
Jurado S, Nebot À, Mugica F, Avellana N (2015). Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques. Energy, 86: 276-291.
Crossref Google Scholar
 
Levy S, Steinberg DM (2010). Computer experiments: a review. AStA Advances in Statistical Analysis, 94: 311-324.
Crossref Google Scholar
 
Li Z, Dai J, Chen H, Lin B (2019). An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage. Building Simulation, 12: 665-681.
Crossref Google Scholar
 
Lomas KJ, Kane T (2013). Summertime temperatures and thermal comfort in UK homes. Building Research and Information, 41: 259-280.
Crossref Google Scholar
 
Lucero-Álvarez J, Martín-Domínguez IR (2017). Effects of solar reflectance and infrared emissivity of rooftops on the thermal comfort of single-family homes in Mexico. Building Simulation, 10: 297-308.
Crossref Google Scholar
 
Makantasi A-M, Mavrogianni A (2016). Adaptation of London’s social housing to climate change through retrofit: a holistic evaluation approach. Advances in Building Energy Research, 10: 99-124.
Crossref Google Scholar
 
McLeod RS, Hopfe CJ, Kwan A (2013). An investigation into future performance and overheating risks in Passivhaus dwellings. Building and Environment, 70: 189-209.
Crossref Google Scholar
 
McLeod RS, Swainson M (2017). Chronic overheating in low carbon urban developments in a temperate climate. Renewable and Sustainable Energy Reviews, 74: 201-220.
Crossref Google Scholar
 
MOHURD (1993). GB50176-93. Code for Thermal Design of Civil Buildings. Ministry of housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2005a). GB50189. Energy-Saving Design Standards for Public Buildings. Ministry of housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2005b). GB 50352-2005. Code for Design of Civil Buildings. Ministry of housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2015). GB50189. Energy-Saving Design Standards For Public Buildings. Ministry of housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2019). Technical Standards for Near-Zero Energy Buildings. Ministry of housing and Urban-Rural Development of China. (in Chinese)
 
Pierangioli L, Cellai G, Ferrise R, Trombi G, Bindi M (2017). Effectiveness of passive measures against climate change: Case studies in Central Italy. Building Simulation, 10: 459-479.
Crossref Google Scholar
 
Ramallo-González AP, Eames ME, Natarajan S, Fosas-de-Pando D, Coley DA (2020). An analytical heat wave definition based on the impact on buildings and occupants. Energy and Buildings, 216: 109923.
Crossref Google Scholar
 
Sala J, Li R, Christensen MH (2019). Clustering and classification of energy meter data: A comparison analysis of data from individual homes and the aggregated data from multiple homes. Building Simulation,
Crossref Google Scholar
 
Silvero F, Lops C, Montelpare S, Rodrigues F (2019). Impact assessment of climate change on buildings in Paraguay—Overheating risk under different future climate scenarios. Building Simulation, 12: 943-960.
Crossref Google Scholar
 
Spentzou E, Cook MJ, Emmitt S (2018). Natural ventilation strategies for indoor thermal comfort in Mediterranean apartments. Building Simulation, 11: 175-191.
Crossref Google Scholar
 
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 Q, Pan Y, Fu Y, Huang Z, Zuo W, Xu P (2019). A simulation-based method for air loop balancing and fan sizing using uncertainty and sensitivity analysis. Building Simulation, 12: 247-258.
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
 
Wang R, Lu S, Feng W (2020a). Impact of adjustment strategies on building design process in different climates oriented by multiple performance. Applied Energy, 266: 114822.
Crossref Google Scholar
 
Wang R, Lu S, Feng W (2020b). A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost. Energy, 192: 116723.
Crossref Google Scholar
 
WMO (2019). Global Climate in 2015-2019. World Meteorological Organization. https://public.wmo.int/en/resources/library/global-climate-2015-2019.
 
Yang X, Zhang S, Xu W (2019). Impact of zero energy buildings on medium-to-long term building energy consumption in China. Energy Policy, 129: 574-586.
Crossref Google Scholar
 
Zhang A, Bokel R, van den Dobbelsteen A, Sun Y, Huang Q, et al. (2017). Optimization of thermal and daylight performance of school buildings based on a multi-objective genetic algorithm in the cold climate of China. Energy and Buildings, 139: 371-384.
Crossref Google Scholar
Building Simulation
Volume 14 Issue 4,
August 2021
Pages 987-1003
DOI: 10.1007/s12273-020-0719-x
Cite this article:
Wang R, Lu S, Feng W, et al. Tradeoff between heating energy demand in winter and indoor overheating risk in summer constrained by building standards. Building Simulation, 2021, 14(4): 987-1003. https://doi.org/10.1007/s12273-020-0719-x

795

Views

21

Crossref

N/A

Web of Science

24

Scopus

2

CSCD

Altmetrics
Received: 25 February 2020
Accepted: 31 August 2020
Published: 06 November 2020
This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020
Return