IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v215y2018icp717-735.html
   My bibliography  Save this article

Impacts of building geometry modeling methods on the simulation results of urban building energy models

Author

Listed:
  • Chen, Yixing
  • Hong, Tianzhen

Abstract

Urban-scale building energy modeling (UBEM)—using building modeling to understand how a group of buildings will perform together—is attracting increasing attention in the energy modeling field. Unlike modeling a single building, which will use detailed information, UBEM generally uses existing building stock data consisting of high-level building information. This study evaluated the impacts of three zoning methods and the use of floor multipliers on the simulated energy use of 940 office and retail buildings in three climate zones using City Building Energy Saver. The first zoning method, OneZone, creates one thermal zone per floor using the target building’s footprint. The second zoning method, AutoZone, splits the building’s footprint into perimeter and core zones. A novel, pixel-based automatic zoning algorithm is developed for the AutoZone method. The third zoning method, Prototype, uses the U.S. Department of Energy’s reference building prototype shapes. Results show that simulated source energy use of buildings with the floor multiplier are marginally higher by up to 2.6% than those modeling each floor explicitly, which take two to three times longer to run. Compared with the AutoZone method, the OneZone method results in decreased thermal loads and less equipment capacities: 15.2% smaller fan capacity, 11.1% smaller cooling capacity, 11.0% smaller heating capacity, 16.9% less heating loads, and 7.5% less cooling loads. Source energy use differences range from -7.6% to 5.1%. When comparing the Prototype method with the AutoZone method, source energy use differences range from -12.1% to 19.0%, and larger ranges of differences are found for the thermal loads and equipment capacities. This study demonstrated that zoning methods have a significant impact on the simulated energy use of UBEM. One recommendation resulting from this study is to use the AutoZone method with floor multiplier to obtain accurate results while balancing the simulation run time for UBEM.

Suggested Citation

  • Chen, Yixing & Hong, Tianzhen, 2018. "Impacts of building geometry modeling methods on the simulation results of urban building energy models," Applied Energy, Elsevier, vol. 215(C), pages 717-735.
  • Handle: RePEc:eee:appene:v:215:y:2018:i:c:p:717-735
    DOI: 10.1016/j.apenergy.2018.02.073
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918301983
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2018.02.073?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chaudhary, Gaurav & New, Joshua & Sanyal, Jibonananda & Im, Piljae & O’Neill, Zheng & Garg, Vishal, 2016. "Evaluation of “Autotune” calibration against manual calibration of building energy models," Applied Energy, Elsevier, vol. 182(C), pages 115-134.
    2. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
    3. Han, Yilong & Taylor, John E. & Pisello, Anna Laura, 2017. "Exploring mutual shading and mutual reflection inter-building effects on building energy performance," Applied Energy, Elsevier, vol. 185(P2), pages 1556-1564.
    4. Lee, Sang Hoon & Hong, Tianzhen & Piette, Mary Ann & Sawaya, Geof & Chen, Yixing & Taylor-Lange, Sarah C., 2015. "Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance," Energy, Elsevier, vol. 90(P1), pages 738-747.
    5. 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.
    6. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    7. Luo, Xuan & Hong, Tianzhen & Chen, Yixing & Piette, Mary Ann, 2017. "Electric load shape benchmarking for small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 715-725.
    8. Hong, Tianzhen & Piette, Mary Ann & Chen, Yixing & Lee, Sang Hoon & Taylor-Lange, Sarah C. & Zhang, Rongpeng & Sun, Kaiyu & Price, Phillip, 2015. "Commercial Building Energy Saver: An energy retrofit analysis toolkit," Applied Energy, Elsevier, vol. 159(C), pages 298-309.
    9. Ramos Ruiz, Germán & Fernández Bandera, Carlos & Gómez-Acebo Temes, Tomás & Sánchez-Ostiz Gutierrez, Ana, 2016. "Genetic algorithm for building envelope calibration," Applied Energy, Elsevier, vol. 168(C), pages 691-705.
    10. 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.
    11. Unternährer, Jérémy & Moret, Stefano & Joost, Stéphane & Maréchal, François, 2017. "Spatial clustering for district heating integration in urban energy systems: Application to geothermal energy," Applied Energy, Elsevier, vol. 190(C), pages 749-763.
    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. Chen, Xi & Yang, Hongxing & Peng, Jinqing, 2019. "Energy optimization of high-rise commercial buildings integrated with photovoltaic facades in urban context," Energy, Elsevier, vol. 172(C), pages 1-17.
    2. Saikia, Pranaynil & Pancholi, Marmik & Sood, Divyanshu & Rakshit, Dibakar, 2020. "Dynamic optimization of multi-retrofit building envelope for enhanced energy performance with a case study in hot Indian climate," Energy, Elsevier, vol. 197(C).
    3. Walsh, Angélica & Cóstola, Daniel & Labaki, Lucila Chebel, 2022. "Performance-based climatic zoning method for building energy efficiency applications using cluster analysis," Energy, Elsevier, vol. 255(C).
    4. Javanroodi, Kavan & Mahdavinejad, Mohammadjavad & Nik, Vahid M., 2018. "Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate," Applied Energy, Elsevier, vol. 231(C), pages 714-746.
    5. 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.
    6. Xavier Faure & Tim Johansson & Oleksii Pasichnyi, 2022. "The Impact of Detail, Shadowing and Thermal Zoning Levels on Urban Building Energy Modelling (UBEM) on a District Scale," Energies, MDPI, vol. 15(4), pages 1-18, February.
    7. Younghoon Kwak & Jeong-A Kang & Jung-Ho Huh & Tae-Hyoung Kim & Young-Sun Jeong, 2019. "An Analysis of the Effectiveness of Greenhouse Gas Reduction Policy for Office Building Design in South Korea," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    8. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2019. "The feasibility and importance of considering climate change impacts in building retrofit analysis," Applied Energy, Elsevier, vol. 233, pages 254-270.
    9. Juan, Yu-Hsuan & Wen, Chih-Yung & Li, Zhengtong & Yang, An-Shik, 2021. "Impacts of urban morphology on improving urban wind energy potential for generic high-rise building arrays," Applied Energy, Elsevier, vol. 299(C).
    10. Shi, Long, 2018. "Theoretical models for wall solar chimney under cooling and heating modes considering room configuration," Energy, Elsevier, vol. 165(PB), pages 925-938.
    11. Ehsan Kamel, 2022. "A Systematic Literature Review of Physics-Based Urban Building Energy Modeling (UBEM) Tools, Data Sources, and Challenges for Energy Conservation," Energies, MDPI, vol. 15(22), pages 1-24, November.
    12. Johari, F. & Peronato, G. & Sadeghian, P. & Zhao, X. & Widén, J., 2020. "Urban building energy modeling: State of the art and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    13. Yanxia Li & Chao Wang & Sijie Zhu & Junyan Yang & Shen Wei & Xinkai Zhang & Xing Shi, 2020. "A Comparison of Various Bottom-Up Urban Energy Simulation Methods Using a Case Study in Hangzhou, China," Energies, MDPI, vol. 13(18), pages 1-23, September.
    14. Chen, Yixing & Deng, Zhang & Hong, Tianzhen, 2020. "Automatic and rapid calibration of urban building energy models by learning from energy performance database," Applied Energy, Elsevier, vol. 277(C).
    15. Jia, Zhiyang & Jin, Xinqiao & Lyu, Yuan & Xue, Qi & Du, Zhimin, 2023. "A robust capacity configuration selection method of multiple-chiller system concerned with the uncertainty of annual hourly load profile," Energy, Elsevier, vol. 282(C).
    16. 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).
    17. Valeria Todeschi & Roberto Boghetti & Jérôme H. Kämpf & Guglielmina Mutani, 2021. "Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    18. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
    19. Garwood, Tom Lloyd & Hughes, Ben Richard & O'Connor, Dominic & Calautit, John K. & Oates, Michael R. & Hodgson, Thomas, 2018. "A framework for producing gbXML building geometry from Point Clouds for accurate and efficient Building Energy Modelling," Applied Energy, Elsevier, vol. 224(C), pages 527-537.
    20. Hye Gi Kim & Hyun Jun Kim & Chae Hwan Jeon & Myeong Won Chae & Young Hum Cho & Sun Sook Kim, 2020. "Analysis of Energy Saving Effect and Cost Efficiency of ECMs to Upgrade the Building Energy Code," Energies, MDPI, vol. 13(18), pages 1-22, September.
    21. Wang, Cheng & Zhu, Ye & Guo, Xiaofeng, 2019. "Thermally responsive coating on building heating and cooling energy efficiency and indoor comfort improvement," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    22. Prataviera, Enrico & Vivian, Jacopo & Lombardo, Giulia & Zarrella, Angelo, 2022. "Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis," Applied Energy, Elsevier, vol. 311(C).
    23. Felix Biessmann & Bhaskar Kamble & Rita Streblow, 2023. "An Automated Machine Learning Approach towards Energy Saving Estimates in Public Buildings," Energies, MDPI, vol. 16(19), pages 1-12, September.
    24. 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. Enríquez, R. & Jiménez, M.J. & Heras, M.R., 2017. "Towards non-intrusive thermal load Monitoring of buildings: BES calibration," Applied Energy, Elsevier, vol. 191(C), pages 44-54.
    2. 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.
    3. Hou, Jin & Xu, Peng & Lu, Xing & Pang, Zhihong & Chu, Yiyi & Huang, Gongsheng, 2018. "Implementation of expansion planning in existing district energy system: A case study in China," Applied Energy, Elsevier, vol. 211(C), pages 269-281.
    4. 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).
    5. Carlos Fernández Bandera & Germán Ramos Ruiz, 2017. "Towards a New Generation of Building Envelope Calibration," Energies, MDPI, vol. 10(12), pages 1-19, December.
    6. Santos, Luis Guilherme Resende & Afshari, Afshin & Norford, Leslie K. & Mao, Jiachen, 2018. "Evaluating approaches for district-wide energy model calibration considering the Urban Heat Island effect," Applied Energy, Elsevier, vol. 215(C), pages 31-40.
    7. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
    8. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2019. "The feasibility and importance of considering climate change impacts in building retrofit analysis," Applied Energy, Elsevier, vol. 233, pages 254-270.
    9. Edwards, Richard E. & New, Joshua & Parker, Lynne E. & Cui, Borui & Dong, Jin, 2017. "Constructing large scale surrogate models from big data and artificial intelligence," Applied Energy, Elsevier, vol. 202(C), pages 685-699.
    10. Chen, Yixing & Deng, Zhang & Hong, Tianzhen, 2020. "Automatic and rapid calibration of urban building energy models by learning from energy performance database," Applied Energy, Elsevier, vol. 277(C).
    11. Rachael Sherman & Hariharan Naganathan & Kristen Parrish, 2021. "Energy Savings Results from Small Commercial Building Retrofits in the US," Energies, MDPI, vol. 14(19), pages 1-16, September.
    12. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    13. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
    14. Glasgo, Brock & Hendrickson, Chris & Azevedo, Inês Lima, 2017. "Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level," Applied Energy, Elsevier, vol. 203(C), pages 348-363.
    15. Jeong, Kwangbok & Hong, Taehoon & Kim, Jimin & Cho, Kyuman, 2019. "Development of a multi-objective optimization model for determining the optimal CO2 emissions reduction strategies for a multi-family housing complex," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 118-131.
    16. Pedro Paulo Fernandes da Silva & Alberto Hernandez Neto & Ildo Luis Sauer, 2021. "Evaluation of Model Calibration Method for Simulation Performance of a Public Hospital in Brazil," Energies, MDPI, vol. 14(13), pages 1-20, June.
    17. 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.
    18. Walsh, Angélica & Cóstola, Daniel & Labaki, Lucila Chebel, 2018. "Performance-based validation of climatic zoning for building energy efficiency applications," Applied Energy, Elsevier, vol. 212(C), pages 416-427.
    19. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    20. Hou, Jing & Liu, Yisheng & Wu, Yong & Zhou, Nan & Feng, Wei, 2016. "Comparative study of commercial building energy-efficiency retrofit policies in four pilot cities in China," Energy Policy, Elsevier, vol. 88(C), pages 204-215.

    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:eee:appene:v:215:y:2018:i:c:p:717-735. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.