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

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 addres...

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Bibliographic Details
Published in:Energies (Basel) 2022-11, Vol.15 (22), p.8689
Main Authors: Ding, Yan, Pan, Xiao, Chen, Wanyue, Tian, Zhe, Wang, Zhiyao, He, Qing
Format: Article
Language:English
Subjects:
Agent based models
agent-based model
Air conditioning
Behavior
Buildings
Control theory
Design optimization
Energy consumption
energy consumption prediction
Energy efficiency
Energy use
Lighting systems
occupant–equipment interaction
Office buildings
Prediction models
Schedules
Simulation
Software
Stochastic models
Stochasticity
two-part model
Wavelet transforms
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Summary: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%.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15228689