Skip to main content
SpringerLink
Log in

Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

  • Cover Article
  • Architecture and Human Behavior
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential. This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable. A case study was conducted for 68,966 buildings in Changsha city, China. First, clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets. Then, the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years. The year built of residential buildings was collected from the housing website. Moreover, twenty-two building types and three vintages were selected as archetype buildings to represent 59,332 buildings, covering 87.4% of the total floor area. Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings. Finally, monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus. The total electricity and natural gas use for the 59,332 buildings was 13,864 GWh and 23.6×106 GJ. Three energy conservation measures were evaluated to demonstrate urban energy saving potential. The proposed methods can be easily applied to other cities in China.

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

Access this article

Log in via an institution

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

  • Ang YQ, Berzolla ZM, Reinhart CF (2020). From concept to application: A review of use cases in urban building energy modeling. Applied Energy, 279: 115738.

    Article  Google Scholar 

  • Anjuke (2021). Pre-owned house. Available at https://cs.anjuke.com/community/. Accessed 3 Jul 2021.

  • Buckley N, Mills G, Reinhart C, et al. (2021). Using urban building energy modelling (UBEM) to support the new European Union’s Green Deal: Case study of Dublin Ireland. Energy and Buildings, 247: 111115.

    Article  Google Scholar 

  • Cerezo Davila C, Reinhart CF, Bemis JL (2016). Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets. Energy, 117: 237–250.

    Article  Google Scholar 

  • Changsha Bureau of Statistics (2019). 2019 Changsha Statistical Yearbook. Beijing: China Statistics Press. (in Chinese)

    Google Scholar 

  • Chen Y, Hong T, Piette MA (2017). 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 

  • Chen Y, Hong T, Luo X, et al. (2019). Development of city buildings dataset for urban building energy modeling. Energy and Buildings, 183: 252–265.

    Article  Google Scholar 

  • Chen Y, Deng Z, Hong T (2020). Automatic and rapid calibration of urban building energy models by learning from energy performance database. Applied Energy, 277: 115584.

    Article  Google Scholar 

  • Ding C, Zhou N (2020). Using residential and office building archetypes for energy efficiency building solutions in an urban scale: A China case study. Energies, 13: 3210.

    Article  Google Scholar 

  • Ding Y, Han S, Tian Z, et al. (2022). Review on occupancy detection and prediction in building simulation. Building Simulation, 15: 333–356.

    Article  Google Scholar 

  • Deng Z, Chen Y, Pan X, et al. (2021). Integrating GIS-based point of interest and community boundary datasets for urban building energy modeling. Energies, 14: 1049.

    Article  Google Scholar 

  • Deng Z, Chen Y (2022). Identification of city-scale building information based on GIS datasets and historical satellite imagery. Journal of Hunan University (Natural Sciences), DOI:https://doi.org/10.16339/j.cnki.hdxbzkb.2022.110. (in Chinese)

  • DOE (2021). Prototype Building Models. U.S. Department of Energy. Available at https://www.energycodes.gov/prototype-building-models. Accessed 1 Sep 2021.

  • Fan C, Yan D, Xiao F, et al. (2021). Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches. Building Simulation, 14: 3–24.

    Article  Google Scholar 

  • Fernandez J, del Portillo L, Flores I (2020). A novel residential heating consumption characterisation approach at city level from available public data: Description and case study. Energy and Buildings, 221: 110082.

    Article  Google Scholar 

  • Ferrando M, Causone F, Hong T, et al. (2020). Urban building energy modeling (UBEM) tools: A state-of-the-art review of bottom-up physics-based approaches. Sustainable Cities and Society, 62: 102408.

    Article  Google Scholar 

  • Gui C, Yan D, Guo S, et al. (2019). Development of prototype building model in Beijing based on actual energy consumption. In: Proceedings of 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019), Harbin, China.

  • Happle G, Fonseca JA, Schlueter A (2018). A review on occupant behavior in urban building energy models. Energy and Buildings, 174: 276–292.

    Article  Google Scholar 

  • Hong T, Chen Y, Luo X, et al. (2020). Ten questions on urban building energy modeling. Building and Environment, 168: 106508.

    Article  Google Scholar 

  • Intelligent Energy Europe Programme (2021). TABULA-Typology Approach for Building Stock Energy Assessment. Available at https://episcope.eu. Accessed 1 Sep 2021.

  • Issermann M, Chang FJ, Kow PY (2021). Interactive urban building energy modelling with functional mockup interface of a local residential building stock. Journal of Cleaner Production, 289: 125683.

    Article  Google Scholar 

  • Li Q, Quan SJ, Augenbroe G, et al. (2015). Building energy modelling at urban scale: Integration of reduced order energy model with geographical information. In: Proceedings of 14th International IBPSA Building Simulation Conference, Hyderabad, India.

  • Li Y (2017). Hot summer and cold winter area middle and primary school teaching buildings energy efficient design research. Master Thesis, Xi’an University of Architecture and Technology, China. (in Chinese)

    Google Scholar 

  • Li F (2018). The diagnosis method research of energy consumption about existing hotel building in Hunan Province. Master Thesis, Hunan University, China. (in Chinese)

    Google Scholar 

  • Li W, Zhou Y, Cetin KS, et al. (2018a). Developing a landscape of urban building energy use with improved spatiotemporal representations in a cool-humid climate. Building and Environment, 136: 107–117.

    Article  Google Scholar 

  • Li X, Yao R, Liu M, et al. (2018b). Developing urban residential reference buildings using clustering analysis of satellite images. Energy and Buildings, 169: 417–429.

    Article  Google Scholar 

  • Li Y, Wang C, Zhu S, et al. (2020a). A comparison of various bottom-up urban energy simulation methods using a case study in Hangzhou, China. Energies, 13: 4781.

    Article  Google Scholar 

  • Li Z, Lin B, Zheng S, et al. (2020b). A review of operational energy consumption calculation method for urban buildings. Building Simulation, 13: 739–751.

    Article  Google Scholar 

  • Lin A (2009). Energy measurement and analysis of hospital buildings in Changsha city. Master Thesis, Hunan University, China. (in Chinese)

    Google Scholar 

  • Liu Y, Tian W, Zhou X (2021). Energy and carbon performance of urban buildings using metamodeling variable importance techniques. Building Simulation, 14: 535–547.

    Article  Google Scholar 

  • Mohammadiziazi R, Copeland S, Bilec MM (2021). Urban building energy model: Database development, validation, and application for commercial building stock. Energy and Buildings, 248: 111175.

    Article  Google Scholar 

  • MOHURD (2001). JGJ 134–2001: Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone. Ministry of Housing and Urban—Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • MOHURD (2003). GB 50096–1999: Design Code for Residential Buildings. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • MOHURD (2004). GB 50034–2004: Standard for Lighting Design of Buildings. Ministry of Housing and Urban—Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • MOHURD (2005). GB 50189–2005: Design Standard for Energy Efficiency of Public Buildings. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • MOHURD (2010). JGJ 134–2010: Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zone. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • MOHURD (2013). GB 50034–2013: Standard for Lighting Design of Buildings. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • MOHURD (2015). GB 50189–2015: Design Standard for Energy Efficiency of Public Buildings. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

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

    Google Scholar 

  • MOHURD (2018). JGJ/T 449–2018: Standard for Green Performance Calculation of Civil Buildings. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • Monteiro CS, 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 

  • National Bureau of Statistics of China (2021). Statistical Communiqué of the People’s Republic of China on the 2020 National Economic and Social Development. Available at http://www.stats.gov.cn/english/PressRelease/202102/t20210228_1814177.html. Accessed 3 Jul 2021.

  • National Renewable Energy Laboratory (2021). OpenStudio-Standards. Available at https://github.com/NREL/openstudio-standards. Accessed 10 Jun 2021.

  • Pasichnyi O, Wallin J, Kordas O (2019). Data-driven building archetypes for urban building energy modelling. Energy, 181: 360–377.

    Article  Google Scholar 

  • Pu Q (2012). Research on prediction model and influencing factors of urban residential building energy consumption. PhD Thesis, Chongqing University, China. (in Chinese)

    Google Scholar 

  • Reinhart CF, Cerezo Davila C (2016). Urban building energy modeling—A review of a nascent field. Building and Environment, 97: 196–202.

    Article  Google Scholar 

  • Shanghai Urban-Rural Construction and Transportation Commission (2009). DG/TJ08–2068-2009: Technical Code for Energy Consumed Monitoring Systems for Large-Scale Public Buildings. (in Chinese)

  • Shen H (2005). Research of energy consumption and energy saving potential for typical public building in Changsha. Master Thesis, Hunan University, China. (in Chinese)

    Google Scholar 

  • Tardioli G, Kerrigan R, Oates M, et al. (2018). Identification of representative buildings and building groups in urban datasets using a novel pre-processing, classification, clustering and predictive modelling approach. Building and Environment, 140: 90–106.

    Article  Google Scholar 

  • Tong Z, Luo Y, Zhou J (2021). Mapping the urban natural ventilation potential by hydrological simulation. Building Simulation, 14: 351–364.

    Article  Google Scholar 

  • Tsinghua University Building Energy Research Centre (2020). 2020 Annual Report on China Building Efficiency. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  • Wang X, Feng W, Cai W, et al. (2019). Do residential building energy efficiency standards reduce energy consumption in China?—A data-driven method to validate the actual performance of building energy efficiency standards. Energy Policy, 131: 82–98.

    Article  Google Scholar 

  • Wei Z (2011). Research on the benchmark tool of public building energy consumption. Master Thesis, China Academy of Building Research, China. (in Chinese)

    Google Scholar 

  • Xu X, Zhang L, Sha H, et al. (2012). Establishment of typical building models in each climate regions in china and research of adaptability of energy saving—Research of adaptability of energy-saving technologies in the standard of 65 % energy saving of buildings. In: Proceedings of Asia IBPSA Building Simulation Conference (ASim2012), Shanghai, China.

  • Xu P, Huang J, Shen P, et al. (2013). Commercial building energy use in six cities in southern China. Energy Policy, 53: 76–89.

    Article  Google Scholar 

  • Yang M (2014). Energy consumption analysis and the application of energy-saving air conditioning system simulation of commercial building complex in hot summer and cold winter zone. Master Thesis, Harbin Institute of Technology, China. (in Chinese)

    Google Scholar 

  • Yang T, Zhang X (2016). Benchmarking the building energy consumption and solar energy trade-offs of residential neighborhoods on Chongming Eco-Island, China. Applied Energy, 180: 792–799.

    Article  Google Scholar 

  • Ye Y, Hinkelman K, Zhang J, et al. (2019). A methodology to create prototypical building energy models for existing buildings: A case study on U.S. religious worship buildings. Energy and Buildings, 194: 351–365.

    Article  Google Scholar 

  • Yuan J (2018). Analysis of energy consumption of hospital buildings and practice of energy-saving transformation. Master Thesis, Southeast University, China. (in Chinese)

    Google Scholar 

  • Zhang Y, Hu S, Yan D, et al. (2021). Exploring cooling pattern of low-income households in urban China based on a large-scale questionnaire survey: A case study in Beijing. Energy and Buildings, 236: 110783.

    Article  Google Scholar 

  • Zhou X, Liu T, Yan D, et al. (2021). An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces. Building Simulation, 14: 301–315.

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by the National Natural Science Foundation of China (NSFC) through Grant No. 51908204 and the Natural Science Foundation of Hunan Province of China through Grant No. 2020JJ3008.

Author information

Authors and Affiliations

  1. College of Civil Engineering, Hunan University, Changsha, 410082, China

    Zhang Deng, Yixing Chen, Jingjing Yang & Zhihua Chen

  2. Key Laboratory of Building Safety and Energy Efficiency of Ministry of Education, Hunan University, Changsha, 410082, China

    Yixing Chen

Authors
  1. Zhang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yixing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingjing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhihua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yixing Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, Z., Chen, Y., Yang, J. et al. Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets. Build. Simul. 15, 1547–1559 (2022). https://doi.org/10.1007/s12273-021-0878-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-021-0878-4

Keywords

Search

Navigation