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

A study on carbon emission calculation in operation stage of residential buildings based on micro electricity usage behavior: Three case studies in China

Menghan Niu1, Ying Ji1( ), Miao Zhao1, Jiefan Gu2, Aonan Li3
Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China
College of Architecture and Urban Planning, Tongji University, Shanghai 201804, China
The Bartlett School of Architecture, University College London, WC1E 6BT, UK
Show Author Information

Abstract

Along with the improvement of social productivity and living standard, residential buildings generate a growing portion of carbon emissions, especially during the operation stage. However, energy use behaviors are usually ignored in carbon emission calculation. This study focuses on calculating carbon emissions during the operation stage for residential buildings based on the characteristics of energy use behaviors in different regions. Firstly, we investigated energy use behaviors in dwellings across three cities in China: Xi’an, Shanghai and Fuzhou. Then, we established calibrated carbon emission models and optimization models with different green building measures for residential buildings. The results of this research reveal a significant disparity between the energy usage habits of residents in different climate regions. The carbon emissions of residential electricity bills in Xi’an, Shanghai and Fuzhou are 13.6 kgCO2/(m2·a) (excluding central heating), 29.3 kgCO2/(m2·a) and 17.2 kgCO2/(m2·a), respectively. Equipment carbon emissions account for 32.2%–64.1% of the total. In comparison to the model based on internal standard setting, the accuracy of the models using actual internal has improved by 25.9%–37.4%. The three-star green building methods have the highest carbon reduction rate among different star buildings, the emission reduction rates are around 30%. This study’s findings are useful for carbon emission calculation and green building design of residential buildings in the future.

Keywords

energy simulationresidential buildingcarbon emissionsenergy usage behavior

References

 

Al-Mumin A, Khattab O, Sridhar G (2003). Occupants’ behavior and activity patterns influencing the energy consumption in the Kuwaiti residences. Energy and Buildings, 35: 549–559.
Crossref Google Scholar
 

Aman MM, Jasmon GB, Mokhlis H, et al. (2013). Analysis of the performance of domestic lighting lamps. Energy Policy, 52: 482–500.
Crossref Google Scholar
 

Bournas I, Dubois M-C (2020). Residential electric lighting use during daytime: A field study in Swedish multi-dwelling buildings. Building and Environment, 180: 106977.
Crossref Google Scholar
 
CABEE (2022). 2022 Research Report of China Building Energy Consumption and Carbon Emissions. Professional Committee of Building Energy and Emissions of China (CABEE). (in Chinese)
 

Cabeza LF, Ürge-Vorsatz D, Palacios A, et al. (2018). Trends in penetration and ownership of household appliances. Renewable and Sustainable Energy Reviews, 82: 4044–4059.
Crossref Google Scholar
 
Chen L (2020). Research on Carbon Emission Calculation and Emission Reduction Strategy of Residential Buildings in the whole life Cycle. Master Thesis, Shenyang Jianzhu University, China. (in Chinese)
 

Chen S, Zhang G, Xia X, et al. (2021). The impacts of occupant behavior on building energy consumption: A review. Sustainable Energy Technologies and Assessments, 45: 101212.
Crossref Google Scholar
 

Chen J, Adhikari R, Wilson E, et al. (2022). Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model. Applied Energy, 325: 119890.
Crossref Google Scholar
 
DBJ 13-62-2019 (2019). DBJ 13-62-2019. Design stndard for energy efficiency of residential buildings in Fujian. Department of Housing and Urban-Rural Development of Fujian Province. (in Chinese)
 
DBJ 61-65-2011 (2012). DBJ 61-65-2011. Design Standard for Energy Efficiency of Residential Buildings. Shaanxi Provincial Architectural Standard Design Office. Department of Housing and Urban-Rural Development of Shaanxi Province. (in Chinese)
 
DGJ 08-205-2015 (2016). DGJ 08-205-2015. Design Standard for Energy Efficiency of Residential Buildings. Shanghai Municipal Commission of Housing and Urban-rural Development. (in Chinese)
 

Diao L, Sun Y, Chen Z, et al. (2017). Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation. Energy and Buildings, 147: 47–66.
Crossref Google Scholar
 

Ding Y, Timoudas TO, Wang Q, et al. (2022). A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries. Energy Conversion and Management, 269: 116163.
Crossref Google Scholar
 

Duan J, Li N, Peng J, et al. (2022). Study on occupant behaviour using air conditioning of high-rise residential buildings in hot summer and cold winter zone in China. Energy and Buildings, 276: 112498.
Crossref Google Scholar
 

Eguaras-Martínez M, Vidaurre-Arbizu M, Martín-Gómez C (2014). Simulation and evaluation of Building Information Modeling in a real pilot site. Applied Energy, 114: 475–484.
Crossref Google Scholar
 
EIA (2020). 2020 Residential Energy Consumption Survey. U.S. Energy Information Administration (EIA).
 

Fumo N, Mago P, Luck R (2010). Methodology to estimate building energy consumption using EnergyPlus Benchmark Models. Energy and Buildings, 42: 2331–2337.
Crossref Google Scholar
 

Geng J, Wang J, Huang J, et al. (2022). Quantification of the carbon emission of urban residential buildings: The case of the Greater Bay Area cities in China. Environmental Impact Assessment Review, 95: 106775.
Crossref Google Scholar
 

Guerra Santin O, Itard L, Visscher H (2009). The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy and Buildings, 41: 1223–1232.
Crossref Google Scholar
 

Han X, Poblete-Cazenave M, Pelz S, et al. (2022). Household energy service and home appliance choices in urban China. Energy for Sustainable Development, 71: 263–278.
Crossref Google Scholar
 

Hassan MA, Shebl SS, Ibrahim EA, et al. (2011). Modeling and validation of the thermal performance of an affordable, energy efficient, healthy dwelling unit. Building Simulation, 4: 255–262.
Crossref Google Scholar
 

He Z, Hong T, Chou SK (2021). A framework for estimating the energy-saving potential of occupant behaviour improvement. Applied Energy, 287: 116591.
Crossref Google Scholar
 

Hong T, Chen Y, Belafi Z, et al. (2018). Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs. Building Simulation, 11: 1–14.
Crossref Google Scholar
 

Hu S, Yan D, Guo S, et al. (2017). A survey on energy consumption and energy usage behavior of households and residential building in urban China. Energy and Buildings, 148: 366–378.
Crossref Google Scholar
 

Huo T, Ren H, Zhang X, et al. (2018). China’s energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method. Journal of Cleaner Production, 185: 665–679.
Crossref Google Scholar
 

Kashif A, Ploix S, Dugdale J, et al. (2013). Simulating the dynamics of occupant behaviour for power management in residential buildings. Energy and Buildings, 56: 85–93.
Crossref Google Scholar
 

Kavgic M, Mavrogianni A, Mumovic D, et al. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, 45: 1683–1697.
Crossref Google Scholar
 

Laskari M, de Masi R-F, Karatasou S, et al. (2022). On the impact of user behaviour on heating energy consumption and indoor temperature in residential buildings. Energy and Buildings, 255: 111657.
Crossref Google Scholar
 
Li B (2012). Research on the technology system and the calculation method of carbon emission of low-carbon building. PhD Thesis, Huazhong University of Science & Technology, China. (in Chinese)
 

Li C, Hong T, Yan D (2014). An insight into actual energy use and its drivers in high-performance buildings. Applied Energy, 131: 394–410.
Crossref Google Scholar
 
Li J (2019). Study on carbon emissions calculation and carbon eduction strategy of urban residential life cycle in cold areas. Master Thesis, Xi’an University of Architecture and Technology, China. (in Chinese)
 

Li S, Foliente G, Seo S, et al. (2021). Multi-scale life cycle energy analysis of residential buildings in Victoria, Australia—A typology perspective. Building and Environment, 195: 107723.
Crossref Google Scholar
 

Li R, Liu Q, Cai W, et al. (2023a). Echelon peaking path of China’s provincial building carbon emissions: Considering peak and time constraints. Energy, 271: 127003.
Crossref Google Scholar
 

Li R, Yu Y, Cai W, et al. (2023b). Interprovincial differences in the historical peak situation of building carbon emissions in China: Causes and enlightenments. Journal of Environmental Management, 332: 117347.
Crossref Google Scholar
 

Liang Y, Pan Y, Yuan X, et al. (2022). Assessment of operational carbon emission reduction of energy conservation measures for commercial buildings: Model development. Energy and Buildings, 268: 112189.
Crossref Google Scholar
 

Liu S, Kwok YT, Lau KKL, et al. (2019). Investigating the energy saving potential of applying shading panels on opaque façades: a case study for residential buildings in Hong Kong. Energy and Buildings, 193: 78–91.
Crossref Google Scholar
 

Liu Z, Liu Y, Wu D, et al. (2020). Performance and feasibility study of solar-air source pump systems for low-energy residential buildings in Alpine regions. Journal of Cleaner Production, 256: 120735.
Crossref Google Scholar
 
MEE (2019). Baseline Emission Factors for China’s Regional Power Grid for the 2019 Emission Reduction Project. Ministry of Ecology and Environment of China. (in Chinese)
 
MOHURD (2016). GB/T 51161-2016. Standard for Energy Consumption of Building. Ministry of Housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2019). GB 50378-2019. Assessment Standard for Green Building in China. Ministry of Housing and Urban-Rural Development of China. (in Chinese)
 
MOHURD (2021). GB 55015-2021. General Code for Energy Efficiency and Renewable Energy Application in Buildings. Ministry of Housing and Urban-Rural Development. (in Chinese)
 
MOHURD (2022). The 14th Five Year Plan for Building Energy Efficiency and Green Building Development. Ministry of Housing and Urban-Rural Development of China. (in Chinese)
 

Nie H, Kemp R (2014). Index decomposition analysis of residential energy consumption in China: 2002–2010. Applied Energy, 121: 10–19.
Crossref Google Scholar
 

Pan Y, Yu C, Long W, et al. (2015). A review of research on regional building load and energy consumption forecasting. Heating Ventilating & Air Conditioning, 45(03): 33–40. (in Chinese)
Google Scholar
 

Pan Y, Liang Y, Zhu M (2021). Review of building carbon emission calculation models in context of carbon neutrality. Heating Ventilating & Air Conditioning, 51(07): 37–48. (in Chinese)
Google Scholar
 

Ren Z, Paevere P, McNamara C (2012). A local-community-level, physically-based model of end-use energy consumption by Australian housing stock. Energy Policy, 49: 586–596.
Crossref Google Scholar
 

Shi R, Li Z, Ji Y (2007). Investigation and researches on residential energy consumption in Shanghai 2002–2004. Refrigeration Air Conditioning & Electric Power Machinery, 14(02): 51–54. (in Chinese)
Crossref Google Scholar
 

Su X, Tian S, Shao X, et al. (2020). Embodied and operational energy and carbon emissions of passive building in HSCW zone in China: A case study. Energy and Buildings, 222: 110090.
Crossref Google Scholar
 

Teng J, Wang W, Mu X, et al. (2021). Assessing energy efficiency of green measures for residential buildings: the simulation case of the Changchun city in China. Journal of Housing and the Built Environment, 36: 1103–1117.
Crossref Google Scholar
 

Wang Z, Zhao Z, Lin B, et al. (2015). Residential heating energy consumption modeling through a bottom-up approach for China’s Hot Summer-Cold Winter climatic region. Energy and Buildings, 109: 65–74.
Crossref Google Scholar
 

Widén J, Nilsson AM, Wäckelgård E (2009). A combined Markov-chain and bottom-up approach to modelling of domestic lighting demand. Energy and Buildings, 41: 1001–1012.
Crossref Google Scholar
 

Yan R, Ma M, et al. (2017). Estimating energy savings in Chinese residential buildings from 2001 to 2015: A decomposition analysis. Journal of Engineering Science and Technology Review, 10: 105–133.
Crossref Google Scholar
 

Yang B (2021). The application of voluntary emission reduction standards in the international carbon market after the Paris Agreement and their regulatory improvement. Trade Explore, 37(6): 102–112. (in Chinese)
Google Scholar
 

Yang J, Deng Z, Guo S, et al. (2023). Development of bottom-up model to estimate dynamic carbon emission for city-scale buildings. Applied Energy, 331: 120410.
Crossref Google Scholar
 

Yu C, Du J, Pan W (2019). Improving accuracy in building energy simulation via evaluating occupant behaviors: A case study in Hong Kong. Energy and Buildings, 202: 109373.
Crossref Google Scholar
 

Yu B (2021). Ecological effects of new-type urbanization in China. Renewable and Sustainable Energy Reviews, 135: 110239.
Crossref Google Scholar
 

Zheng X, Wei C, Yu Y, et al. (2017). Household Energy Consumption in China: 2016 Report. Beijing: Science Press. (in Chinese)
Building Simulation
Volume 17 Issue 1,
January 2024
Pages 147-164
DOI: 10.1007/s12273-023-1070-9
Cite this article:
Niu M, Ji Y, Zhao M, et al. A study on carbon emission calculation in operation stage of residential buildings based on micro electricity usage behavior: Three case studies in China. Building Simulation, 2024, 17(1): 147-164. https://doi.org/10.1007/s12273-023-1070-9

265

Views

3

Crossref

2

Web of Science

2

Scopus

0

CSCD

Altmetrics
Received: 11 June 2023
Revised: 27 July 2023
Accepted: 14 August 2023
Published: 17 October 2023
© Tsinghua University Press 2023
Return