IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8689-d977663.html
   My bibliography  Save this article

Prediction Method for Office Building Energy Consumption Based on an Agent-Based Model Considering Occupant–Equipment Interaction Behavior

Author

Listed:
  • Yan Ding

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Key Laboratory of Efficient Utilisation of Low and Medium Grade Energy, MOE, Tianjin University, Tianjin 300072, China)

  • Xiao Pan

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China)

  • Wanyue Chen

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China)

  • Zhe Tian

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Key Laboratory of Efficient Utilisation of Low and Medium Grade Energy, MOE, Tianjin University, Tianjin 300072, China)

  • Zhiyao Wang

    (Tianjin ANJIE IOT Science and Technology Co., Ltd., Tianjin 300392, China)

  • Qing He

    (Tianjin ANJIE IOT Science and Technology Co., Ltd., Tianjin 300392, China)

Abstract

Traditional building energy consumption prediction methods lack the description of occupant behaviors. The interactions between occupants and equipment have great influence on building energy consumption, which cause a large deviation between the predicted results and the actual situation. To address this problem, a two-part prediction model, consisting of a basic part related to the building area and a variable part related to stochastic occupant behaviors, is proposed in this study. The wavelet decomposition and reconstruction method is firstly used to split the energy consumption. A relationship between the low frequency energy consumption data and the building area is discovered, and an area-based index is used to fit the basic part of the prediction model. With a quantitative description of the occupant–equipment interaction by classifying the equipment into environmentally relevant and environmentally irrelevant equipment, an agent-based model is established in the variable part. According to the validation given by two case office buildings, the prediction error can be controlled to 2.8% and 10.1%, respectively, for the total and the hourly building energy consumption. Compared to the prediction method which does not consider occupant–equipment interactions, the proposed model can improve prediction accuracy by 55.8%.

Suggested Citation

  • Yan Ding & Xiao Pan & Wanyue Chen & Zhe Tian & Zhiyao Wang & Qing He, 2022. "Prediction Method for Office Building Energy Consumption Based on an Agent-Based Model Considering Occupant–Equipment Interaction Behavior," Energies, MDPI, vol. 15(22), pages 1-31, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8689-:d:977663
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8689/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8689/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, S. & Kim, A.A. & Johnson, E.M., 2017. "Understanding the deterministic and probabilistic business cases for occupant based plug load management strategies in commercial office buildings," Applied Energy, Elsevier, vol. 191(C), pages 398-413.
    2. Jia, Mengda & Srinivasan, Ravi S. & Raheem, Adeeba A., 2017. "From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 525-540.
    3. Cheng, Yuanda & Gao, Min & Dong, Jiankai & Jia, Jie & Zhao, Xudong & Li, Guiqiang, 2018. "Investigation on the daylight and overall energy performance of semi-transparent photovoltaic facades in cold climatic regions of China," Applied Energy, Elsevier, vol. 232(C), pages 517-526.
    4. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    5. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    6. Ahmadi-Karvigh, Simin & Ghahramani, Ali & Becerik-Gerber, Burcin & Soibelman, Lucio, 2018. "Real-time activity recognition for energy efficiency in buildings," Applied Energy, Elsevier, vol. 211(C), pages 146-160.
    7. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    8. Ghahramani, Ali & Pantelic, Jovan & Lindberg, Casey & Mehl, Matthias & Srinivasan, Karthik & Gilligan, Brian & Arens, Edward, 2018. "Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems," Applied Energy, Elsevier, vol. 230(C), pages 42-51.
    9. Romuald Élie & Emma Hubert & Thibaut Mastrolia & Dylan Possamaï, 2021. "Mean–field moral hazard for optimal energy demand response management," Mathematical Finance, Wiley Blackwell, vol. 31(1), pages 399-473, January.
    10. Bertrand, Alexandre & Mastrucci, Alessio & Schüler, Nils & Aggoune, Riad & Maréchal, François, 2017. "Characterisation of domestic hot water end-uses for integrated urban thermal energy assessment and optimisation," Applied Energy, Elsevier, vol. 186(P2), pages 152-166.
    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. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    2. Anand, Prashant & Cheong, David & Sekhar, Chandra & Santamouris, Mattheos & Kondepudi, Sekhar, 2019. "Energy saving estimation for plug and lighting load using occupancy analysis," Renewable Energy, Elsevier, vol. 143(C), pages 1143-1161.
    3. Xu, Xiaoxiao & Yu, Hao & Sun, Qiuwen & Tam, Vivian W.Y., 2023. "A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    4. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    5. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    6. Gigih Rahmandhani Setyantho & Hansaem Park & Seongju Chang, 2021. "Multi-Criteria Performance Assessment for Semi-Transparent Photovoltaic Windows in Different Climate Contexts," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    7. Habtamu Tkubet Ebuy & Hind Bril El Haouzi & Riad Benelmir & Remi Pannequin, 2023. "Occupant Behavior Impact on Building Sustainability Performance: A Literature Review," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    8. Moe Soheilian & Géza Fischl & Myriam Aries, 2021. "Smart Lighting Application for Energy Saving and User Well-Being in the Residential Environment," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    9. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    10. Anti Hamburg & Targo Kalamees, 2018. "The Influence of Energy Renovation on the Change of Indoor Temperature and Energy Use," Energies, MDPI, vol. 11(11), pages 1-15, November.
    11. Jakob Carlander & Bahram Moshfegh & Jan Akander & Fredrik Karlsson, 2020. "Effects on Energy Demand in an Office Building Considering Location, Orientation, Façade Design and Internal Heat Gains—A Parametric Study," Energies, MDPI, vol. 13(23), pages 1-22, November.
    12. Alencastro, João & Fuertes, Alba & de Wilde, Pieter, 2018. "The relationship between quality defects and the thermal performance of buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 883-894.
    13. Imran & Naeem Iqbal & Shabir Ahmad & Do Hyeun Kim, 2021. "Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    14. Prasanna, Ashreeta & Dorer, Viktor & Vetterli, Nadège, 2017. "Optimisation of a district energy system with a low temperature network," Energy, Elsevier, vol. 137(C), pages 632-648.
    15. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    16. Zhong, Shengyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Deng, Shuai & Li, Yang & Hussain, Sajjad & Wang, Xiaoyuan & Zhu, Jiebei, 2021. "Quantitative analysis of information interaction in building energy systems based on mutual information," Energy, Elsevier, vol. 214(C).
    17. Eugene Mohareb & Arman Hashemi & Mehdi Shahrestani & Minna Sunikka-Blank, 2017. "Retrofit Planning for the Performance Gap: Results of a Workshop on Addressing Energy, Health and Comfort Needs in a Protected Building," Energies, MDPI, vol. 10(8), pages 1-17, August.
    18. 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.
    19. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    20. Pierryves Padey & Kyriaki Goulouti & Guy Wagner & Blaise Périsset & Sébastien Lasvaux, 2021. "Understanding the Reasons behind the Energy Performance Gap of an Energy-Efficient Building, through a Probabilistic Approach and On-Site Measurements," Energies, MDPI, vol. 14(19), pages 1-15, September.

    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:15:y:2022:i:22:p:8689-:d:977663. 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.