TY - GEN
T1 - Energy efficient control methods of HVAC systems for smart campus
AU - Petrie, Caleb
AU - Gupta, Smit
AU - Rao, Vittal
AU - Nutter, Brian
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/5
Y1 - 2018/6/5
N2 - 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.
AB - 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.
KW - Advanced controls for HVAC systems
KW - Building energy management systems
KW - Energy saving by minimizing simultaneous heating and cooling
UR - http://www.scopus.com/inward/record.url?scp=85048979536&partnerID=8YFLogxK
U2 - 10.1109/GreenTech.2018.00032
DO - 10.1109/GreenTech.2018.00032
M3 - Conference contribution
AN - SCOPUS:85048979536
T3 - IEEE Green Technologies Conference
SP - 133
EP - 136
BT - Proceedings - 2018 IEEE Annual Green Technologies Conference, GreenTech 2018
PB - IEEE Computer Society
T2 - 2018 IEEE Annual Green Technologies Conference, GreenTech 2018
Y2 - 4 April 2018 through 6 April 2018
ER -