Tools for Auto-Calibration of Building Energy Models and Predictive Control

With rising energy costs and finite supply, Energy Conservation Measures (ECMs) are becoming increasingly important in the United States, particularly in regard to buildings, which account for 40% of the total energy used in America. Currently, energy use in buildings can be assessed through energy-modeling software during the design process or during post-occupancy once the building is commissioned, but these metrics are often autonomous from one-another, and not comprehensive. The overarching goal of this proposal is to develop High Performance Computing (HPC) support tools that address energy-efficiency and performance demands in buildings. This would be accomplished by creating an executive software platform that would monitor and make decisions based on input from (1) a sensor network integrated into a physical building and (2) energy-modeling simulations run on a computer, working in collaboration. The executive program would accurately calibrate the energy model to the physical building, provide fault and damage detection to building managers, and establish predictive controls to optimize building operation. Commercially, this proposal would inform energy-efficient building design and operation. It would benefit engineers, architects, property managers, real estate investors, and homeowners.