Reducing Building Energy Performance Gap: Integrating Agent-Based Modelling and Building Performance Simulation
The building energy performance gap (BEPG) remains a significant challenge, undermining the accuracy of energy simulations and complicating efforts to design energy-efficient buildings. This study addresses this issue by developing an adaptive occupant behaviour framework for office buildings, integ...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/10/1728 |
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| Summary: | The building energy performance gap (BEPG) remains a significant challenge, undermining the accuracy of energy simulations and complicating efforts to design energy-efficient buildings. This study addresses this issue by developing an adaptive occupant behaviour framework for office buildings, integrating agent-based modelling (ABM) with a building performance simulation (BPS) platform. Conventional BPS models often rely on deterministic assumptions and overlook the dynamic, stochastic nature of occupant interactions, such as window and blind operations. By incorporating occupant-driven behaviours, this research enhances the realism of energy predictions and provides insights into reducing the BEPG. Focusing on a multi-functional office building at the University of Nottingham, the study used empirical data to validate the model. The ABM framework simulated occupant behaviours influenced by factors like indoor and outdoor temperatures, solar radiation, clothing levels, and metabolic rates. Profiles generated by the ABM were integrated into the energy model, creating an Adjust model compared against a Base model with deterministic settings. Validation against measured boiler energy use showed that the Baseline model over-predicted consumption by roughly 45 %, whereas the behaviour-informed Adjust model cut the deviation to about 26 %, albeit under-predicting the total load. Statistical analyses revealed improvements in mean squared error (MSE) and root mean squared error (RMSE), although hourly energy predictions remained a challenge. Additionally, the Adjust model provided a more realistic representation of thermal comfort, reducing variability in the predicted mean vote (PMV) index from extreme values in the Base model to a more stable range in the Adjust model. However, the Adjust model also predicted higher indoor CO<sub>2</sub> concentrations, particularly in individual offices, due to reduced ventilation associated with occupant actions. This study demonstrates the potential of integrating ABM with BPS models to address modelling discrepancies by capturing detailed and dynamic occupant interactions, emphasising the importance of adaptive behaviours in improving prediction accuracy and occupant well-being. |
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| ISSN: | 2075-5309 |