Energy Efficient Control Methods of HVAC Systems for Smart Campus

Abstract

The incorporation of advanced sensor data acquisition techniques, concepts of cyber physical systems, and the Internet of Things (IoT) devices into infrastructures and building energy management systems is enabling the smart city deployments. The main emphasis of this paper is to design and implementation of energy efficient controls into the heating, ventilation, and air conditioning (HVAC) systems of the commercial buildings. These systems are responsible for providing acceptable indoor air quality and thermal comfort to the occupants. By tuning and implementing advanced control systems into the existing HVAC systems, energy consumption can be reduced by 20-30%. The traditional control techniques are compared with two advanced control techniques: Pattern Recognition Adaptive Controller (PRAC) and Model Predictive Control (MPC). The salient features of the advanced control techniques are presented. By utilizing the existing hardware and software systems, the proposed control techniques are implemented on one of the academic buildings of the Texas Tech University campus. The preliminary results obtained in the minimization of simultaneous heating and cooling and energy savings of the HVAC systems are presented in the paper.