IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v146y2021ics1364032121004639.html
   My bibliography  Save this article

Grey-box modeling and application for building energy simulations - A critical review

Author

Listed:
  • Li, Yanfei
  • O'Neill, Zheng
  • Zhang, Liang
  • Chen, Jianli
  • Im, Piljae
  • DeGraw, Jason

Abstract

Grey-box modeling, as one of the three fundamental modeling techniques for building energy models, has many advantages compared with black-box modeling and white-box modeling. It has been widely applied to solve problems of building technologies, such as building load estimation, control and optimization, and building-grid integration. However, a thorough review of grey-box modeling is not available. This review study systematically investigated various aspects of grey-box modeling for buildings. First, the fundamental aspects of grey-box modeling are presented, including the theoretical background, modeling of building elements, modeling order, modeling diagram, and order reduction. Second, the detailed modeling approaches are discussed. Third, multiple applications of grey-box modeling are investigated for building energy domain, which are categorized into the following groups: heat dynamics analysis, thermal load estimation, building control and optimization, district/urban scale energy modeling, and building-grid integration. Finally, the available software packages for grey-box modeling are compared. Overall, the challenges of using grey-box modeling can be summarized as follows: (1) the theoretical limitations and assumptions of grey-box modeling are unclear; (2) grey-box model naming convention and structure are confusing; (3) grey-box model creation is vague; (4) suitable applications of grey-box models are unknown; and (5) grey-box models lack unified software solutions for wider adoption.

Suggested Citation

  • Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
  • Handle: RePEc:eee:rensus:v:146:y:2021:i:c:s1364032121004639
    DOI: 10.1016/j.rser.2021.111174
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032121004639
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2021.111174?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. Michalak, Piotr, 2014. "The simple hourly method of EN ISO 13790 standard in Matlab/Simulink: A comparative study for the climatic conditions of Poland," Energy, Elsevier, vol. 75(C), pages 568-578.
    2. Shan, Kui & Wang, Jiayuan & Hu, Maomao & Gao, Dian-ce, 2019. "A model-based control strategy to recover cooling energy from thermal mass in commercial buildings," Energy, Elsevier, vol. 172(C), pages 958-967.
    3. Razmara, M. & Bharati, G.R. & Hanover, Drew & Shahbakhti, M. & Paudyal, S. & Robinett, R.D., 2017. "Building-to-grid predictive power flow control for demand response and demand flexibility programs," Applied Energy, Elsevier, vol. 203(C), pages 128-141.
    4. Kim, Eui-Jong & He, Xi & Roux, Jean-Jacques & Johannes, Kévyn & Kuznik, Frédéric, 2019. "Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling," Applied Energy, Elsevier, vol. 238(C), pages 963-971.
    5. Harish, V.S.K.V. & Kumar, Arun, 2016. "Reduced order modeling and parameter identification of a building energy system model through an optimization routine," Applied Energy, Elsevier, vol. 162(C), pages 1010-1023.
    6. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    7. Luo, Yongqiang & Zhang, Ling & Liu, Zhongbing & Wang, Yingzi & Meng, Fangfang & Wu, Jing, 2016. "Thermal performance evaluation of an active building integrated photovoltaic thermoelectric wall system," Applied Energy, Elsevier, vol. 177(C), pages 25-39.
    8. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    9. Mirakhorli, Amin & Dong, Bing, 2018. "Model predictive control for building loads connected with a residential distribution grid," Applied Energy, Elsevier, vol. 230(C), pages 627-642.
    Full references (including those not matched with items on IDEAS)

    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. Sivaneasan, Balakrishnan & Kandasamy, Nandha Kumar & Lim, May Lin & Goh, Kwang Ping, 2018. "A new demand response algorithm for solar PV intermittency management," Applied Energy, Elsevier, vol. 218(C), pages 36-45.
    2. Wei, Ziqing & Ren, Fukang & Zhu, Yikang & Yue, Bao & Ding, Yunxiao & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2022. "Data-driven two-step identification of building thermal characteristics: A case study of office building," Applied Energy, Elsevier, vol. 326(C).
    3. Yucheng Guo & Jie Shi & Tong Guo & Fei Guo & Feng Lu & Lingqi Su, 2024. "Grey-Box Method for Urban Building Energy Modelling: Advancements and Potentials," Energies, MDPI, vol. 17(21), pages 1-25, October.
    4. Shen, Pengyuan & Braham, William & Yi, Yunkyu, 2018. "Development of a lightweight building simulation tool using simplified zone thermal coupling for fast parametric study," Applied Energy, Elsevier, vol. 223(C), pages 188-214.
    5. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    6. 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).
    7. Joe, Jaewan & Im, Piljae & Cui, Borui & Dong, Jin, 2023. "Model-based predictive control of multi-zone commercial building with a lumped building modelling approach," Energy, Elsevier, vol. 263(PA).
    8. Yoon, Ah-Yun & Kim, Young-Jin & Zakula, Tea & Moon, Seung-Ill, 2020. "Retail electricity pricing via online-learning of data-driven demand response of HVAC systems," Applied Energy, Elsevier, vol. 265(C).
    9. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    10. Shan, Kui & Wang, Shengwei & Zhuang, Chaoqun, 2021. "Controlling a large constant speed centrifugal chiller to provide grid frequency regulation: A validation based on onsite tests," Applied Energy, Elsevier, vol. 300(C).
    11. Marzullo, Thibault & Keane, Marcus M. & Geron, Marco & Monaghan, Rory F.D., 2019. "A computational toolchain for the automatic generation of multiple Reduced-Order Models from CFD simulations," Energy, Elsevier, vol. 180(C), pages 511-519.
    12. Xiao, Lan & Qin, Liang-Liang & Wu, Shuang-Ying, 2023. "Effect of PV-Trombe wall in the multi-storey building on standard effective temperature (SET)-based indoor thermal comfort," Energy, Elsevier, vol. 263(PB).
    13. Ascione, Fabrizio & De Masi, Rosa Francesca & de Rossi, Filippo & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2016. "Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study," Applied Energy, Elsevier, vol. 183(C), pages 938-957.
    14. Jefferson Brooks & Ana Rivera & Miguel Chen Austin & Nathalia Tejedor-Flores, 2022. "A Machine Learning-Based Approach to Estimate Energy Flows of the Mangrove Forest: The Case of Panama Bay," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    15. Bay, Christopher J. & Chintala, Rohit & Chinde, Venkatesh & King, Jennifer, 2022. "Distributed model predictive control for coordinated, grid-interactive buildings," Applied Energy, Elsevier, vol. 312(C).
    16. Niemelä, Tuomo & Kosonen, Risto & Jokisalo, Juha, 2016. "Cost-optimal energy performance renovation measures of educational buildings in cold climate," Applied Energy, Elsevier, vol. 183(C), pages 1005-1020.
    17. César Benavente-Peces & Nisrine Ibadah, 2020. "Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers," Energies, MDPI, vol. 13(13), pages 1-24, July.
    18. Hau, Lee Cheun & Lim, Yun Seng & Liew, Serena Miao San, 2020. "A novel spontaneous self-adjusting controller of energy storage system for maximum demand reductions under penetration of photovoltaic system," Applied Energy, Elsevier, vol. 260(C).
    19. Zhao, Dongliang & Yin, Xiaobo & Xu, Jingtao & Tan, Gang & Yang, Ronggui, 2020. "Radiative sky cooling-assisted thermoelectric cooling system for building applications," Energy, Elsevier, vol. 190(C).
    20. Yin, Ershuai & Li, Qiang & Xuan, Yimin, 2018. "Optimal design method for concentrating photovoltaic-thermoelectric hybrid system," Applied Energy, Elsevier, vol. 226(C), pages 320-329.

    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:rensus:v:146:y:2021:i:c:s1364032121004639. 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/600126/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.