TY - GEN
T1 - Model predictive control for HVAC systems - A review
AU - Kwadzogah, Roger
AU - Zhou, Mengchu
AU - Li, Sisi
PY - 2013
Y1 - 2013
N2 - The world faces an energy problem. Oil supply is gradually running out. Its use is polluting the planet with greenhouse gas. Most alternative energy sources also pose some environmental problems. Hence the efficient use of energy must be an integral part of any solution to them. Whenever possible, one must use advanced control engines and technologies to achieve the highest energy efficiency. In Heating Ventilation and Air Conditioning (HVAC) industry, much research has identified a number of energy-saving control methods. Derived from optimal control theory, Model Predictive Control (MPC) is one of them. This paper presents a comprehensive review of its applications to HVAC systems.
AB - The world faces an energy problem. Oil supply is gradually running out. Its use is polluting the planet with greenhouse gas. Most alternative energy sources also pose some environmental problems. Hence the efficient use of energy must be an integral part of any solution to them. Whenever possible, one must use advanced control engines and technologies to achieve the highest energy efficiency. In Heating Ventilation and Air Conditioning (HVAC) industry, much research has identified a number of energy-saving control methods. Derived from optimal control theory, Model Predictive Control (MPC) is one of them. This paper presents a comprehensive review of its applications to HVAC systems.
UR - http://www.scopus.com/inward/record.url?scp=84891534421&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84891534421&partnerID=8YFLogxK
U2 - 10.1109/CoASE.2013.6654072
DO - 10.1109/CoASE.2013.6654072
M3 - Conference contribution
AN - SCOPUS:84891534421
SN - 9781479915156
T3 - IEEE International Conference on Automation Science and Engineering
SP - 442
EP - 447
BT - 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
T2 - 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
Y2 - 17 August 2013 through 20 August 2013
ER -