International Journal of Control, Automation, and Systems 2025; 23(2): 581-591
https://doi.org/10.1007/s12555-024-0463-6
© The International Journal of Control, Automation, and Systems
Power consumption has been increasing significantly due to the earth’s rising global temperatures, leading to higher energy usage in HVAC systems. Choosing the conventional setpoint temperature could reduce unnecessary power consumption, thus leading to cost savings. This paper extends the design of supervisory model predictive control (SMPC) for HVAC systems with multiple zones. The design objective aims to shave the peak demand and maintain occupants’ thermal comfort. Two methods of SMPC are developed, namely, centralized SMPC and decentralized SMPC. Previously, SMPC was developed using the standard quadratic programming (QP) solver and the active set method. In this paper, we apply the sparse QP solver using the interior point method. The results indicate that centralized supervisory control (SC) yields better outcomes, as demonstrated by a trade-off curve between total operating costs and thermal comfort. Moreover, centralized model predictive control (MPC) successfully achieved satisfactory results in both tracking the reference signal and optimizing power consumption. Utilizing the sparse QP solver can yield faster computation compared to the standard QP solver, making it more suitable for the design of SMPC.
Keywords Centralized control, decentralized control, HVAC system, interior point method, model predictive control, multi-zones, sparse quadratic programming, supervisory control.
International Journal of Control, Automation, and Systems 2025; 23(2): 581-591
Published online February 1, 2025 https://doi.org/10.1007/s12555-024-0463-6
Copyright © The International Journal of Control, Automation, and Systems.
David Banjerdpongchai* and Pasitnat Sasananand
Chulalongkorn University
Power consumption has been increasing significantly due to the earth’s rising global temperatures, leading to higher energy usage in HVAC systems. Choosing the conventional setpoint temperature could reduce unnecessary power consumption, thus leading to cost savings. This paper extends the design of supervisory model predictive control (SMPC) for HVAC systems with multiple zones. The design objective aims to shave the peak demand and maintain occupants’ thermal comfort. Two methods of SMPC are developed, namely, centralized SMPC and decentralized SMPC. Previously, SMPC was developed using the standard quadratic programming (QP) solver and the active set method. In this paper, we apply the sparse QP solver using the interior point method. The results indicate that centralized supervisory control (SC) yields better outcomes, as demonstrated by a trade-off curve between total operating costs and thermal comfort. Moreover, centralized model predictive control (MPC) successfully achieved satisfactory results in both tracking the reference signal and optimizing power consumption. Utilizing the sparse QP solver can yield faster computation compared to the standard QP solver, making it more suitable for the design of SMPC.
Keywords: Centralized control, decentralized control, HVAC system, interior point method, model predictive control, multi-zones, sparse quadratic programming, supervisory control.
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