AI Integration in Building Data Platforms: Enabling Proactive Fault Detection and Energy Conservation

Abstract

The increasing complexity and energy demands of modern buildings necessitate smarter, more autonomous systems capable of maintaining operational efficiency while minimizing environmental impact. Traditional Building Management Systems (BMS) often fall short in delivering timely insights for fault detection and energy optimization due to siloed data and reactive maintenance approaches. This paper explores the integration of Artificial Intelligence (AI) within building data platforms to enable proactive fault detection and intelligent energy conservation strategies. By leveraging machine learning algorithms, real-time sensor analytics, and pattern recognition models, the proposed framework can identify anomalies in HVAC, lighting, and power systems with high accuracy before they escalate into major failures. A real-world implementation within a commercial facility demonstrates a measurable reduction in energy consumption and a significant drop in unplanned maintenance events. Quantitative analysis reveals improvements in fault detection precision, energy efficiency, and system responsiveness. The research underscores the transformative potential of AI-enhanced platforms in advancing smart building operations and sets the stage for scalable, adaptive infrastructure capable of self-optimization in future urban environments