SBIR Phase I: Smart Control Automation and Learning for Energy

Award ID: 2221872

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a commercial building management system that uses model predictive control for small to mid-sized commercial building owners to help them flexibly manage increasingly complex energy codes and prices. This technology uses machine learning to automate costly aspects of advanced building control, and eliminating complexity, frustration, and expense for leanly staffed building owners who are attempting to save money, meet code, reduce carbon footprint and adapt to rapidly changing energy prices. The proposed approach significantly reduces the setup time, the amount of training data, and the compute time needed for the technology to converge on accurate models and predictions using building thermal dynamics. These improvements reduce the controller costs without sacrificing accuracy. This technology will simplify the setup and implementation process for under-represented segments in the building automation, efficiency and model-based controls market starting with K-12 schools. This simplification has several distinct societal and environmental benefits including: increased energy and demand charge savings, increased energy efficiency, improved environmental footprint, increased job creation for building controls technicians, improved resiliency, and additional educational opportunities for K-12 families and communities. This SBIR Phase I project develops a technology capable of building efficiency control. The innovation employs a hybrid approach based on constrained deep learning tools that build on physical knowledge of building systems and architecture, thereby making use of sampling data while producing physics-consistent accuracy in modeling and control predictions. Specifically, the project team hopes to converge on an architecture that can more reliably and accurately manage energy use and occupant comfort compared to state of the art control approaches. The project team also aims to demonstrate a significant reduction in heating, ventilation and air-conditioning (HVAC)-driven peak system demand in target buildings while keeping instrumentation, labor, and data costs per building to an affordable cost for the target market. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Vincent Lee