Bayesian-Based Stochastic Model for Integrating Occupancy and Building Characteristics in Residential Energy Vulnerability Analysis

Residential energy vulnerability analysis usually requires the integration of building characteristics and occupancy profiles. However, current research often fails to catch the dynamic relationships between building features and occupancy behaviors. To address these gaps, this study proposes a Bayesian-based stochastic integration model to analyze energy vulnerability by integrating occupancy profiles with building features. The framework integrates multiple data sources to establish probabilistic linkages between key demographic vulnerabilities (e.g., the elderly, poverty, and medical electricity dependency) and building attributes (e.g., construction year, heating fuel, and building size). Then, a Bayesian beta regression approach is employed to generate and evaluate occupancy-building combinations, enabling the identification of high-risk groups and sensitive building typologies. This research highlights the role of integrated modeling in addressing gaps in vulnerability assessment and informing equitable energy interventions for sustainable residential energy systems.