BEHAVIOR-DRIVEN RETROFIT STRATEGIES FOR RESIDENTIAL BUILDING ENVELOPES: A CO-SIMULATION AND AGENT-BASED MODELING STUDY

ABSTRACT Buildings play a significant role in global energy consumption, making it crucial to understand the interplay between energy usage and occupant behavior. While behavior-centric approaches to building sustainability are widely advocated, the incorporation of human behavior in Performance-Based Design (PBD) remains underexplored. This study aims to develop a co-simulation model to evaluate the impact of various retrofit strategies for residential building envelopes, taking into account realistic and stochastic occupancy schedules and diverse occupant behavior patterns. To achieve this objective, a hybrid approach was employed, involving surveys, classification, clustering processes, and a co-simulation framework that integrates Rhino Grasshopper (GH) and Python. Within this framework, custom Python components were used to implement agent-based modeling (ABM), enabling the representation of occupant agents with distinct behavioral rules, awareness levels, and interaction patterns. The results reveal significant differences in the influence of customized occupancy schedules on the effectiveness of retrofit strategies, highlighting the variability in energy savings potential based on distinct energy consumption patterns and behaviors. For example, Energy Use Intensity (EUI) analysis showed reductions of 55.40%, 68.90%, and 94.10% compared to the base case (210.368 kWh/m 2 /year) under three representative stochastic occupancy scenarios (A1, A2, and A3). While a particular retrofit strategy may yield positive outcomes under one occupancy scenario, it may be less effective or even counterproductive under another. These findings underscore the importance of incorporating detailed occupancy schedules, energy consumption patterns, behaviors, and awareness rates in retrofitting buildings and conducting passive building designs to maximize energy savings potential. This research contributes to the field by demonstrating the critical role of occupant behavior in optimizing retrofit strategies for residential buildings.