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

  • Shuqin Chen
  • Jun GuanEmail author
  • Natasa Nord
  • Nianping Li
  • Hiroshi Yoshino
Original Article


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.


Urban residential energy information system Citywide building energy management Cluster analysis Case study 



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).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. Chen, Y. X., Hong, T. Z., & Luo, X. (2017b). An agent-based stochastic occupancy simulator. Building Simulation, 11(1), 1–13.Google Scholar
  8. Comprehensive financial affairs department of ministry of construction (2005). Urban construction statistical bulletin in the year of 2004., 2005-05-10. Accessed11.05.14.
  9. Dong, W. Q., Zhou, G. Y., & Xia, L. X. (1979). Quantification theory and its application. Changchun: Jilin Renmin Press.Google Scholar
  10. European commission (2017). EU building stock observatory. Accessed 20.11.17.
  11. 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.CrossRefGoogle Scholar
  12. Granderson, J., Piette, M. A., & Ghatikar, G. (2011). Building energy information systems: user case studies. Energ Efficiency, 4, 17–30.CrossRefGoogle Scholar
  13. Harbin Statistical Bureau. (2004). Harbin statistical yearbook 2004. Beijing: China Statistics Press.Google Scholar
  14. Hong Kong Official Languages Agency (HKOLA) (2001). Hong Kong yearbook 2001—land, public engineering and public utilities. 2001-08. Accessed11.05.14.
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. Hörner, M., & Lichtmeß, M. (2018). Energy performance of buildings: a statistical approach to marry calculated demand and measured consumption. Energy Efficiency.
  19. Hunan Statistical Bureau. (2003). Hunan statistical yearbook 2003. Beijing: China Statistics Press.Google Scholar
  20. Kunming Statistical Bureau. (2006). Kunming statistical yearbook 2006. Beijing: China Statistics Press.Google Scholar
  21. 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
  22. 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.Google Scholar
  23. MATLAB (2014). (Version R2013a) Math Works. Accessed11.05.14.
  24. 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.CrossRefGoogle Scholar
  25. 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. Accessed 24.06.08.
  26. 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.CrossRefGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. National Bureau of Statistics of China. (2018). China statistical yearbook 2017. Beijing: China Statistics Press.Google Scholar
  29. Nie, H., & Kemp, R. (2014). Index decomposition analysis of residential energy consumption in China: 2002–2010. Applied Energy, 121, 10–19.CrossRefGoogle Scholar
  30. Office of Energy Efficiency & Renewable Energy, Department of Energy, U.S.A (2017) Building performance database. Accessed10.4.17.
  31. 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.CrossRefGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. Streets, D. G., & Waldhoff, S. T. (2000). Present and future emissions of air pollutants in China. Atmospheric Environment, 34, 363–374.CrossRefGoogle Scholar
  35. U.S Energy Information Administration (2016a). Commercial Buildings Energy Consumption Survey. Http://Www.Eia.Gov/Consumption/Commercial/. Accessed 08.08.16.
  36. U.S Energy Information Administration (2016b). Residential Energy Consumption Survey. Accessed 08.08.16.
  37. 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.CrossRefGoogle Scholar
  38. Urumqi Statistical Bureau. (2005). Urumqi statistical yearbook 2005. Beijing: China Statistics Press.Google Scholar
  39. Wu, X. F., & Chen, G. Q. (2017). Energy use by Chinese economy: a systems cross-scale input-output analysis. Energy Policy, 108, 81–90.CrossRefGoogle Scholar
  40. Xiang, Y. Q. (2000). The handbook of common data in the gas thermodynamic project. Beijing: Chinese Architecture and Building Press.Google Scholar
  41. 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.CrossRefGoogle Scholar
  42. Xue, W. (2001). Statistical analysis and the application of SPSS. Beijing: China Renmin University Press.Google Scholar
  43. 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.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Shuqin Chen
    • 1
  • Jun Guan
    • 2
    Email author
  • Natasa Nord
    • 3
  • Nianping Li
    • 4
  • Hiroshi Yoshino
    • 5
  1. 1.College of Civil Engineering and ArchitectureZhejiang UniversityHangzhouChina
  2. 2.School of Energy and Power EngineeringNanjing University of Science and TechnologyNanjingChina
  3. 3.Department of Energy and Process EngineeringNorwegian University of Science and TechnologyTrondheimNorway
  4. 4.Civil Engineering CollegeHunan UniversityChangshaChina
  5. 5.Graduate School of EngineeringTohoku UniversitySendaiJapan

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