Leveraging AI-Optimization at the Edge for Optimal Comfort and Energy Efficiency

Facility managers and building owners face numerous challenges in maintaining energy efficiency and occupant comfort in diverse building environments. Traditional HVAC systems often struggle with optimizing performance due to static control strategies and lack of real-time adaptability. This paper explores the potential of AI deployed “at the edge” to address these challenges. By leveraging AI-driven algorithms within Schneider Electric's room controllers, the aim is to enhance HVAC system control and optimization. The research involved deploying room controller units without AI to collect baseline data, followed by enabling AI to monitor and improve system performance. The findings demonstrate that AI can significantly enhance energy efficiency and occupant comfort by providing adaptive, efficient, and user-centric solutions directly from the edge: within the device, eliminating the added expense and complexity of cloud-based processing. Quantified results show that AI-enabled systems maintained temperature regulation and comfort compliance over 85% of the time, and that average energy saved on HVAC reached 5% daily, highlighting the effectiveness of AI in real-world applications.