Methods for Dynamic Network Identification with Application to the Control of Smart Buildings

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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. Although 'dynamic system identification' is a well-developed field, the field of identification of dynamic networks is not at all well-developed. Traditional dynamic system identification techniques cannot exploit the inherent sparseness of the network identification problem, while traditional machine learning techniques are mostly applicable to only static networks. In this project we combine ideas from traditional dynamic system identification, L1 optimization for sparse vector recovery (from compressed sensing), and graphical modeling from machine learning to address the challenges in dynamic network identification. If successful, the research will (1) provide fundamental contribution to the nascent field of dynamic network identification through new algorithms, and (2) enable speedy deployment of 'smart building' technologies in commercial buildings. In addition, the project will support a number of educational innovations for attracting students from under-represented groups to engineering and generating excitement about engineering.