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

Yan Ding (), Xiao Pan, Wanyue Chen, Zhe Tian, Zhiyao Wang and Qing He
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Yan Ding: School of Environmental Science and Engineering, 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
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

Energies, 2022, vol. 15, issue 22, 1-31

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%.

Keywords: energy consumption prediction; office buildings; two-part model; agent-based model; occupant–equipment interaction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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