IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3210-d374250.html
   My bibliography  Save this article

Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study

Author

Listed:
  • Chao Ding

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94709, USA)

  • Nan Zhou

    (Lawrence Berkeley National Laboratory, Berkeley, CA 94709, USA)

Abstract

Building energy consumption accounts for 36% of the overall energy end use worldwide and is growing rapidly as developing countries continue to urbanize. Understanding the energy use at urban scale will lay the foundation for identification of energy efficiency opportunities to be deployed at speed. China has almost half of global new constructions and plays an important role in building suitability. However, an open source national building energy consumption database is not available in China. To provide data support for building energy consumptions, this paper used a simulation method to develop an urban building energy consumption database for a pilot city in Wuhan, China. First, residential, small, and large office building archetype energy models were created in EnergyPlus to represent typical building energy consumption in Wuhan. The baseline reference model simulation results were further validated using survey data from the literature. Second, stochastic simulations were conducted to consider different design parameters and occupants’ energy usage intensity scenarios, such as thermal properties of the building envelope, lighting power density, equipment power density, HVAC (heating, ventilation and air conditioning) schedule, etc. A building energy consumption database was generated for typical building archetypes. Third, data-driven regression analysis was conducted to support quick building energy consumption prediction using key high- level building information inputs. Finally, a web-based urban energy platform and an interface were developed to support further third-party application development. The research is expected to provide fast energy efficiency building design solutions for urban planning, new constructions as well as building retrofits.

Suggested Citation

  • Chao Ding & Nan Zhou, 2020. "Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study," Energies, MDPI, vol. 13(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3210-:d:374250
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3210/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3210/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    2. Cerezo Davila, Carlos & Reinhart, Christoph F. & Bemis, Jamie L., 2016. "Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets," Energy, Elsevier, vol. 117(P1), pages 237-250.
    3. Zhou, Nan & Price, Lynn & Yande, Dai & Creyts, Jon & Khanna, Nina & Fridley, David & Lu, Hongyou & Feng, Wei & Liu, Xu & Hasanbeigi, Ali & Tian, Zhiyu & Yang, Hongwei & Bai, Quan & Zhu, Yuezhong & Xio, 2019. "A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030," Applied Energy, Elsevier, vol. 239(C), pages 793-819.
    4. A.M. Fogheri, 2015. "Energy Efficiency in Public Buildings," Rivista economica del Mezzogiorno, Società editrice il Mulino, issue 3-4, pages 763-784.
    5. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    6. Nan Zhou & Nina Khanna & Wei Feng & Jing Ke & Mark Levine, 2018. "Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050," Nature Energy, Nature, vol. 3(11), pages 978-984, November.
    7. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Xia & Fang, Yuan & Cai, Weiguang & Ding, Chao & Xie, Yupei, 2022. "Heating demand with heterogeneity in residential households in the hot summer and cold winter climate zone in China -A quantile regression approach," Energy, Elsevier, vol. 247(C).
    2. Wang, Xia & Ding, Chao & Cai, Weiguang & Luo, Lizi & Chen, Mingman, 2021. "Identifying household cooling savings potential in the hot summer and cold winter climate zone in China: A stochastic demand frontier approach," Energy, Elsevier, vol. 237(C).
    3. Luo, Jianing & Yuan, Yanping & Joybari, Mahmood Mastani & Cao, Xiaoling, 2024. "Development of a prediction-based scheduling control strategy with V2B mode for PV-building-EV integrated systems," Renewable Energy, Elsevier, vol. 224(C).
    4. Tatjana Vilutienė & Rasa Džiugaitė-Tumėnienė & Diana Kalibatienė & Darius Kalibatas, 2021. "How BIM Contributes to a Building’s Energy Efficiency throughout Its Whole Life Cycle: Systematic Mapping," Energies, MDPI, vol. 14(20), pages 1-27, October.
    5. Wang, Xia & Ding, Chao & Zhou, Mao & Cai, Weiguang & Ma, Xianrui & Yuan, Jiachen, 2023. "Assessment of space heating consumption efficiency based on a household survey in the hot summer and cold winter climate zone in China," Energy, Elsevier, vol. 274(C).
    6. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    7. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    2. Zhang Deng & Yixing Chen & Xiao Pan & Zhiwen Peng & Jingjing Yang, 2021. "Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling," Energies, MDPI, vol. 14(4), pages 1-17, February.
    3. Ali, Usman & Shamsi, Mohammad Haris & Bohacek, Mark & Purcell, Karl & Hoare, Cathal & Mangina, Eleni & O’Donnell, James, 2020. "A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making," Applied Energy, Elsevier, vol. 279(C).
    4. Prataviera, Enrico & Zarrella, Angelo & Morejohn, Joshua & Narayanan, Vinod, 2024. "Exploiting district cooling network and urban building energy modeling for large-scale integrated energy conservation analyses," Applied Energy, Elsevier, vol. 356(C).
    5. Perwez, Usama & Yamaguchi, Yohei & Ma, Tao & Dai, Yanjun & Shimoda, Yoshiyuki, 2022. "Multi-scale GIS-synthetic hybrid approach for the development of commercial building stock energy model," Applied Energy, Elsevier, vol. 323(C).
    6. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    7. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    8. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Guglielmina Mutani & Valeria Todeschi & Simone Beltramino, 2020. "Energy Consumption Models at Urban Scale to Measure Energy Resilience," Sustainability, MDPI, vol. 12(14), pages 1-31, July.
    10. Katal, Ali & Mortezazadeh, Mohammad & Wang, Liangzhu (Leon), 2019. "Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations," Applied Energy, Elsevier, vol. 250(C), pages 1402-1417.
    11. Jiang, Jingjing & Ye, Bin & Liu, Junguo, 2019. "Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 813-833.
    12. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).
    13. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    14. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
    15. Nagpal, Shreshth & Hanson, Jared & Reinhart, Christoph, 2019. "A framework for using calibrated campus-wide building energy models for continuous planning and greenhouse gas emissions reduction tracking," Applied Energy, Elsevier, vol. 241(C), pages 82-97.
    16. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    17. Kazas, Georgios & Fabrizio, Enrico & Perino, Marco, 2017. "Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study," Applied Energy, Elsevier, vol. 193(C), pages 243-262.
    18. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
    19. Huo, Tengfei & Xu, Linbo & Liu, Bingsheng & Cai, Weiguang & Feng, Wei, 2022. "China’s commercial building carbon emissions toward 2060: An integrated dynamic emission assessment model," Applied Energy, Elsevier, vol. 325(C).
    20. Wenliang Li, 2020. "Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data," Energies, MDPI, vol. 13(12), pages 1-22, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3210-:d:374250. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.