Skip to main content

Advertisement

Log in

A study of citywide urban residential energy information system for the building energy efficiency management: a cluster model of seven typical cities in China

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

The lack of empirical data demonstrating the relationship between influencing factors and building energy performance is one of the primary barriers in energy efficiency management. A citywide residential energy information database and the data-based analytical methodology help increase the knowledge about the local real estate situation, explore energy efficiency opportunities and measures, financial investment, and market trend in the local building stocks, and make the reasonable policies as well. Few databases were established in USA and Europe only covering the building information and energy use, while there are lack of an indices system and database of building energy efficiency information in China. Therefore, in this study, a definition of urban residential energy information system is suggested, covering the parameters of building characteristics, household characteristics, possession and operation of domestic appliances, indoor thermal environment, climate condition, energy market, economic level, municipal infrastructure, and energy use consequence. Consequently, a database is developed to collect the raw data in seven typical cities in China. A classification model is established by Quantitative Theory III to classify and characterize the urban residential energy use systems into three different city groups, and suggestions are made to guide the energy efficiency work for different city groups. The case study is a good example to demonstrate the methodology and the analysis provided a helpful reference for the citywide building energy efficiency management.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Chen, Y. X., & Hong, T. Z. (2018). Impacts of building geometry modeling methods on the simulation results of urban building energy models. Applied Energy, 215, 717–735.

    Article  Google Scholar 

  • Chen, S., Li, N., Guan, J., Xie, Y., Sun, F., & Ni, J. (2008). A statistical method to investigate national energy consumption in the residential building sector of China. Energy and Building, 40, 654–665.

    Article  Google Scholar 

  • Chen, S., Yoshino, H., & Li, N. (2010). Statistical analyses on summer energy consumption characteristics of residential buildings in some cities of China. Energy and Building, 42, 136–146.

    Article  Google Scholar 

  • Chen, S., Levine, M. D., Yoshino, H., et al. (2013). Total energy use in buildings: Analysis and evaluation methods. Vol (I): definitions and reporting. Tokyo: Institute for Building Environment and Energy Conservation.

    Google Scholar 

  • Chen, S., Yang, W., Yoshino, H., Levine, M. D., Newhouse, K., & Hinge, A. (2015). Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies. Energy and Buildings, 104, 1–13.

    Article  Google Scholar 

  • Chen, Y. X., Liang, X., Hong, T. Z., et al. (2017a). Simulation and visualization of energy-related occupant behavior in office buildings. Building Simulation, 10, 785–798.

    Article  Google Scholar 

  • Chen, Y. X., Hong, T. Z., & Luo, X. (2017b). An agent-based stochastic occupancy simulator. Building Simulation, 11(1), 1–13.

    Google Scholar 

  • Comprehensive financial affairs department of ministry of construction (2005). Urban construction statistical bulletin in the year of 2004. http://www.ynist.gov.cn/article/2005051074555113.htm, 2005-05-10. Accessed11.05.14.

  • Dong, W. Q., Zhou, G. Y., & Xia, L. X. (1979). Quantification theory and its application. Changchun: Jilin Renmin Press.

    Google Scholar 

  • European commission (2017). EU building stock observatory. https://ec.europa.eu/energy/en/eubuildings. Accessed 20.11.17.

  • Feng, D., Sovacool, B., & Vu, K. (2010). The barriers to energy efficiency in China: assessing household electricity savings and consumer behavior in Liaoning Province. Energ Policy, 38, 1202–1209.

    Article  Google Scholar 

  • Granderson, J., Piette, M. A., & Ghatikar, G. (2011). Building energy information systems: user case studies. Energ Efficiency, 4, 17–30.

    Article  Google Scholar 

  • Harbin Statistical Bureau. (2004). Harbin statistical yearbook 2004. Beijing: China Statistics Press.

    Google Scholar 

  • Hong Kong Official Languages Agency (HKOLA) (2001). Hong Kong yearbook 2001—land, public engineering and public utilities. http://www.yearbook.gov.hk/2001/chtml/13/index.htm. 2001-08. Accessed11.05.14.

  • Hong, T. Z., Taylor-Lange, S. C., D’Oca, S., et al. (2016). Advances in research and applications of energy-related occupant behavior in buildings. Energy and Buildings, 116, 694–702.

    Article  Google Scholar 

  • Hong, T., Chen, Y. X., & Piette, M. A. (2017a). Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis. Applied Energy, 205, 323–335.

    Article  Google Scholar 

  • Hong, T. Z., Yan, D., D'Oca, S., & Chen, C. (2017b). Ten questions concerning occupant behavior in buildings: the big picture. Building and Environment, 114, 518–530.

    Article  Google Scholar 

  • Hörner, M., & Lichtmeß, M. (2018). Energy performance of buildings: a statistical approach to marry calculated demand and measured consumption. Energy Efficiency. https://doi.org/10.1007/s12053-018-9664-2.

  • Hunan Statistical Bureau. (2003). Hunan statistical yearbook 2003. Beijing: China Statistics Press.

    Google Scholar 

  • Kunming Statistical Bureau. (2006). Kunming statistical yearbook 2006. Beijing: China Statistics Press.

    Google Scholar 

  • Li, J. X., Wang, C. M., Wang, G. C., & Liu, W. (2010). Analysis landside influential factors and coupling intensity based on the third theory of quantification. Chinese Journal of Rock Mechanics and Engineering, 29(6), 1206–1213.

    Google Scholar 

  • Mathew P. A., Dunn L. N., Sohn M. D., et al. (2015) . Big-data for building energy performance: Lessons from assembling a very large national database of building energy use. Applied Energy, 140:85–93.

  • MATLAB (2014). (Version R2013a) Math Works. http://www.mathworks.se. Accessed11.05.14.

  • McNeil, M. A., Feng, W., Can, S., et al. (2016). Energy efficiency outlook in China’s urban buildings sector through 2030. Energy Policy, 97, 532–539.

    Article  Google Scholar 

  • Ministry of Housing and Urban–Rural Development of China (2008). The notice to publicize the related technic guidelines of the construction of energy consumption monitoring systems of state office buildings and large office buildings. http://www.mohurd.gov.cn/zcfg/jsbwj_0/jsbwjjskj/200807/t20080702_174380.html. Accessed 24.06.08.

  • Monteiro, C. S., Costa, C., Pina, A., et al. (2018). An urban building database (UBD) supporting a Smart City Information system. Energy and Buildings, 158, 244–260.

    Article  Google Scholar 

  • Morris, J., Allinson, D., Harrison, J., et al. (2016). Benchmarking and tracking domestic gas and electricity consumption at the local authority level. Energy Efficiency, 9, 723–743.

    Article  Google Scholar 

  • National Bureau of Statistics of China. (2018). China statistical yearbook 2017. Beijing: China Statistics Press.

    Google Scholar 

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

    Article  Google Scholar 

  • Office of Energy Efficiency & Renewable Energy, Department of Energy, U.S.A (2017) Building performance database. https://energy.gov/eere/buildings/building-performance-database. Accessed10.4.17.

  • Palmer, K., & Walls, M. (2017). Using information to close the energy efficiency gap: a review of benchmarking and disclosure ordinances. Energy Efficiency, 10, 73–691.

    Article  Google Scholar 

  • Singh, M. K., Mahapatra, S., & Teller, J. (2013). An analysis on energy efficiency initiatives in the building stock of Liege, Belgium. Energy Policy, 62, 729–741.

    Article  Google Scholar 

  • Stanley, S., Lyons, R. C., & Lyons, S. (2016). The price effect of building energy rating in the Dublin residential market. Energ Efficiency, 9, 875–885.

    Article  Google Scholar 

  • Streets, D. G., & Waldhoff, S. T. (2000). Present and future emissions of air pollutants in China. Atmospheric Environment, 34, 363–374.

    Article  Google Scholar 

  • U.S Energy Information Administration (2016a). Commercial Buildings Energy Consumption Survey. Http://Www.Eia.Gov/Consumption/Commercial/. Accessed 08.08.16.

  • U.S Energy Information Administration (2016b). Residential Energy Consumption Survey. http://www.eia.gov/consumption/residential/index.cfm. Accessed 08.08.16.

  • Ueno, T., Sano, F., Saeki, O., & Tsuji, K. (2006). Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data. Applied Energy, 83, 166–183.

    Article  Google Scholar 

  • Urumqi Statistical Bureau. (2005). Urumqi statistical yearbook 2005. Beijing: China Statistics Press.

    Google Scholar 

  • Wu, X. F., & Chen, G. Q. (2017). Energy use by Chinese economy: a systems cross-scale input-output analysis. Energy Policy, 108, 81–90.

    Article  Google Scholar 

  • Xiang, Y. Q. (2000). The handbook of common data in the gas thermodynamic project. Beijing: Chinese Architecture and Building Press.

    Google Scholar 

  • Xu, P., Xu, T., & Shen, P. (2013). Energy and behavioral impacts of integrative retrofits for residential buildings: what is at stake for building energy policy reforms in northern China? Energy Policy, 52, 667–676.

    Article  Google Scholar 

  • Xue, W. (2001). Statistical analysis and the application of SPSS. Beijing: China Renmin University Press.

    Google Scholar 

  • Yu, Z., Fung, B. C. M., Haghighat, F., Yoshino, H., & Morofsky, E. (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43, 1409–1417.

    Article  Google Scholar 

  • Zhou, N., Mcneil, M., & Levine, M. (2012). Assessment of building energy-saving policies and programs in China during the 11th five-year plan. Energy Efficiency, 5, 51–64.

    Article  Google Scholar 

  • Zhou, N., Fridley, D., Zheng, N. K., et al. (2013). China’s energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model. Energy Policy, 53, 51–62.

    Article  Google Scholar 

Download references

Acknowledgements

This paper was supported by the China National Key R&D Program (Grant No. 2018YFC0704400), National Natural Science Foundation of China (grant no. 51508500), and State Key Laboratory of Subtropical Building Science (South China University of Technology, Grant No. 2018ZB17).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jun Guan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Guan, J., Nord, N. et al. A study of citywide urban residential energy information system for the building energy efficiency management: a cluster model of seven typical cities in China. Energy Efficiency 12, 1509–1528 (2019). https://doi.org/10.1007/s12053-018-9768-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-018-9768-8

Keywords

Navigation