Autonomous Thermal Performance Optimization of Vacuum Insulation Panels (VIPs) Through Multi-Modal Data Fusion and Bayesian Network Inference

**Abstract:** This paper introduces a novel methodology for optimizing the long-term thermal performance of Vacuum Insulation Panels (VIPs) employed in zero-energy building applications. VIPs, while offering superior insulation compared to conventional materials, suffer from degradation over time, leading to reduced performance and potential system failures. This research leverages a multi-modal data fusion approach, incorporating spectroscopic analysis, acoustic emission monitoring, and high-resolution thermal imaging, coupled with Bayesian Network inference to create a predictive model for VIP degradation. The system dynamically adjusts insulation system parameters, such as sealant composition and structural support configurations, to mitigate degradation and maintain optimal thermal performance throughout the VIP's lifespan, ultimately minimizing energy consumption and maximizing building efficiency. This approach lowers maintenance costs, elevates energy efficiency, and introduces robust monitoring capabilities for energy-efficient building components.