ET26SWE0019 - AI-driven HVAC & Lighting Integration

This project proposes a field demonstration of an emerging technology (“ET”) which is a commercial-ready technology that integrates networked lighting controls with heating, ventilation, and air conditioning (“HVAC”) systems. Using open-source protocols, the ET enables communication between lighting sensors and smart thermostats, with locally run machine learning algorithms optimizing HVAC performance based on real-time occupancy and historical data. This project will assess energy savings, occupant comfort, system compatibility, and performance in small to medium commercial buildings, where HVAC systems often lack cost-effective controls. The goal of this project is to demonstrate a scalable, Title 24-compliant solution that improves overall efficiency and supports broader adoption of integrated systems across buildings of all sizes.

The market faces several challenges in achieving optimal energy efficiency through integrated systems. Firstly, while lighting controls have advanced significantly, their integration with HVAC systems remains largely unexplored, resulting in missed opportunities for enhanced energy efficiency. Secondly, the industry is fragmented, with HVAC and lighting contractors typically operating in separate domains, leading to a lack of unified solutions that leverage data from both systems for building energy optimization. Additionally, there is limited adoption of smart energy management technology in small commercial buildings. While large buildings benefit from Building Automation and Control Network (“BACnet”)-based solutions, small commercial spaces with packaged HVAC units lack access to similar technology. Lastly, significant energy savings opportunities are missing. Studies from the Department of Energy (“DOE”) (PNNL-22072) and American Society of Heating, Refrigerating and Air-Conditioning Engineers (“ASHRAE”) confirm that leveraging occupancy data for HVAC control can yield energy savings of 10-25% (DOE 2021), yet no widespread solution exists to implement this effectively in small commercial buildings. Addressing these challenges is crucial for realizing the full potential of integrated energy management systems in commercial buildings.

To assist market adoption, this project will seek cost-effective and scalable solutions for integrated systems while providing energy savings. A previous DOE study found the lighting and HVAC integration was highly cost effective and reduced the simple payback of the lighting system by 39% (DOE 2021). Another case study “Exploring HVAC and Light Controls Integration and Interoperability” integrated lighting and HVAC controls for a small to medium-sized commercial building found significant energy savings of 60%. To integrate HVAC control, the study used Wi-Fi and setpoints to integrate with lighting control mesh networks (mwConnect 2024). While the case study’s methodology is different from this proposed project’s ET, the concept is the same. The report highlighted financial incentives that could significantly offset the project costs such as Sacramento Municipal Utility District (“SMUD”) rebated and tax credits. These case studies showcase the potential of Artificial Intelligence (“AI”)-driven lighting and HVAC systems to enhance energy efficiency and operational performance in small to medium commercial buildings.