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<p>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.</p>

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A Sparse Quadratic Programming Approach and Interior Point Method to Design Supervisory Model Predictive Control of Multi-zone HVAC Systems

  • 期刊论文

  • 发表日期：

    2025年2月5日

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  • David Banjerdpongchai,
  • Pasitnat Sasananand

摘要

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.