Grant: Alliance Grants

The need for improved energy efficiency in buildings is increasing as concerns over GHG emissions rise and uncertainty over future energy costs. Monitoring of building systems is valuable to gather information but despite substantial research to develop artificial intelligence (AI)-supported controls systems, commercial systems are typically limited to simpler algorithms and require significant time to deploy and tailor these systems to a particular building, along with seasonal adjustment. Therefore, the development of automated control tuning using advanced AI approaches to permit online learning and seasonal adjustment is extremely desirable. This project extends a previous ENGAGE grant that demonstrated the value of this approach for heating systems and replicate it for cooling season optimization, to minimize energy use while maintaining thermal comfort conditions. We will build on previous advances in heating algorithm development developed under the Engage grant and test the applications in ongoing field studies to inform future refinements and incorporate more aggressive weather-adjusted control predictions. Once the heating season is over, we will collect chiller and building cooling system data to develop both a predictive load model and set of control point optimization models for the cooling systems for Multi-Unit Residential Buildings. These algorithms will be developed and deployed using a cloud-based building monitoring and control system, with updated algorithms provided to the industry partner on a monthly basis starting in Month 4 of the grant to permit in-situ testing and ongoing refinement during the cooling system. By working with existing facilities, the algorithms will be tested and deployed on pilot projects to confirm actual energy savings in real buildings.