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

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  • 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
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    References listed on IDEAS

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    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
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