Integrating building performance simulation in agent-based modeling using regression surrogate models: A novel human-in-the-loop energy modeling approach

Abstract Building Performance Simulation (BPS) is an established method used in the design phase of buildings to predict energy consumption and guide design choices. Despite their advanced abilities to model complex building systems, BPS tools typically fail to account for different and changing energy use characteristics of building occupants, contributing to important prediction errors. In parallel, Agent-Based Modeling (ABM) has emerged in recent years as a technique capable of capturing occupants’ dynamic energy consumption behaviors and actions. However, ABM lacks the building simulation capabilities to account for the complexity of various building systems in energy calculations. This research proposes a new modeling framework that integrates BPS in ABM using trained regression surrogate models. The framework is unique in its ability to (1) simulate energy use attributes of building occupants and facility managers, (2) translate those attributes to robust energy consumption estimates, and (3) help quantify the impact of uncertainty in human actions on the performance of the built environment. The framework is tested and illustrated in a case study on a prototype office building. Results indicate that providing occupants with control over their building systems can mitigate the effect of uncertainty in human actions on the performance of the built environment.