AI in Building Operations:
Insights from Our Talk at UC Davis (CLTC)

Earlier this month, KenergyAI had the opportunity to speak at UC Davis (CLTC) about the current state of artificial intelligence in building operations and where the technology is headed. We want to thank Professor Jae Suk for the invitation. The discussion focused on practical applications available today, lessons from real deployments, and how AI can support more efficient and responsive buildings without compromising occupant comfort.

The conversation began with an overview of where AI stands in commercial and institutional buildings. While interest in AI has grown quickly, many solutions remain limited by complex integration requirements, high implementation costs, or approaches that risk comfort complaints. In contrast, software-only platforms that work with existing building systems are beginning to deliver measurable results with lower disruption.

KenergyAI demonstrated how its AI platform optimizes HVAC systems by using real-time occupancy data and learned patterns. The system adjusts setpoints and deadbands primarily during unoccupied periods, then returns control to the building’s existing sequences when spaces are in use. This approach reduces energy waste while preserving the comfort strategies facilities teams already rely on. Case studies reviewed during the talk showed meaningful reductions in HVAC energy use and peak demand, achieved without changes to equipment or core control sequences.

The presentation also covered OpenNLC, KenergyAI’s solution for light commercial buildings that rely on thermostats rather than full building management systems. OpenNLC connects occupancy signals from networked lighting controls or standalone sensors to smart thermostats, extending occupancy-based optimization to a much larger portion of the building's stock. Field results and demonstration projects were shared to illustrate how the technology performs in real operating conditions.

A key highlight of the session was a forward-looking discussion on the future of AI in building operations. Beyond today’s focus on occupancy-driven setbacks and schedule integration, KenergyAI believes AI will eventually be capable of fully controlling buildings. Future systems are expected to combine occupancy data, schedules, weather, equipment behavior, and real-time performance feedback to manage HVAC, lighting, and related systems in a coordinated and adaptive way. The long-term vision is AI that can operate buildings more efficiently and reliably while still supporting the goals of facilities teams and occupants.

KenergyAI continues to expand both commercial deployments and research-oriented work. Organizations interested in exploring these capabilities further are invited to reach out regarding free pilot projects or collaboration on AI research in building operations.