Principal Investigator: Prabir Barooah

Sponsor: National Science Foundation

Start Date: June 1, 2015

End Date: May 31, 2020

Amount: $354,895

Abstract

A dynamic network consists of interacting dynamic sub-systems. Such networks occur in many domains: living cells, financial markets, the Internet and the power grid are some examples. Heating, ventilation and air conditioning (HAVC) systems in buildings can also be modeled through dynamic networks since each room’s climate depends on that of nearby spaces. Knowledge of such dynamic network models is essential to design and deploy control strategies devoted to the improvement of energy efficiency and occupant comfort. Yet, in practice the structure and dynamics of these networks are either unknown or imprecisely known. For instance, information on the thermal interaction among rooms is difficult to obtain from laws of physics due to the complexity of the physical processes involved. The goal of this project is to formulate algorithms for the identification of dynamic sparse network models from measured data. The research results will support the study of advanced controls for HVAC systems to reduce their energy use and to provide demand-side flexibility to the power grid. Since buildings consume 75% of the nation’s electricity, improvement of energy efficiency through smart building control systems will contribute to the sustainability of the nation’s energy system.