Identification of the Optimal Control Strategies for the Energy-Efficient Ventilation for the Model Predictive Control

Abstract The significant energy use and resultant air pollutants emissions from the HVAC system pose grave concerns to the global society. The model predictive control (MPC) approach is found to be an effective and economic way to optimally regulate the operational parameters of the HVAC system. In this study, the authors focus on the control of the ventilation of the HVAC system under two objectives: minimal energy consumption and high degree of indoor thermal comfort. The authors present a first comprehensive study to investigate the influences of cost function formulations on MPC control of the overall performance, and manage to identify the optimal cost function design for the ventilation control. This study incorporates the non-linear power predictive model and PMV calculations into the cost function in addition to the traditional linearized power and PMV models. The results indicate that with non-linear power and PMV calculations, the MPC controller could perform much better in terms of both the energy consumption and indoor thermal comfort. By defining conversion efficiency as the ratio between PMV change and energy consumption decrease, the optimal control strategy, proposed by the authors, can lead to a range of 29.2% to 49.8% efficiency elevation depending on the application scenarios.