HVAC system study: a data-driven approach

The energy consumed by heating, ventilating, and air conditioning (HVAC) systems has increased in the past two decades. Thus, improving efficiency of HVAC systems has gained more and more attentions. This concern has posed challenges for modeling and optimizing HVAC systems. The traditional methods, such as analytical and statistical methods, are usually computationally complex and involve assumptions that may not hold in practice since HVAC system is a complex, nonlinear, and dynamic system. Data-mining approach is a novel science aiming at extracting system characteristics, identifying models and recognizing patterns from large-size data set. It has proved its power in modeling complex and nonlinear systems through various effective and successful applications in industrial, business, and medical areas. Classical data-mining techniques, such as neural networks and boosting tree have been largely applied into modeling HVAC systems in literature. Evolutionary computation, including swarm intelligence, have rapidly developed in the past decades and then applied to improving the performance of HVAC systems.

This research focuses on modeling, optimizing, and controlling an HVAC system. Data-mining algorithms are first utilized to extract predictive models from experimental data set at Energy Resource Station in Ankeney. Evolutionary algorithms are then employed to solve the optimization models converted from the above data-driven models. In the optimization process, two set points of the HVAC system, supply air duct static pressure set point and supply air temperature set point, are controlled aiming at improving the energy efficiency and maintaining thermal comfort. The methodology presented in this research is applicable to various industrial processes other than HVAC systems.