Development and Data-Driven Evaluation of Internal Heat Gain Prediction-Based MPC for HVAC Systems

초록

This study presents a method for continuously predicting internal heat gains (IHG) using plug and lighting power data, which are generallymore accessible than occupancy data. In addition, a procedure is introduced for effectively integrating these dynamic IHG predictions into amulti-objective Model Predictive Control (MPC) framework. To reflect the dynamic behavior of IHG in real buildings, a Modelica simulationenvironment was developed using long-term measured data on occupancy, plug loads, and lighting loads. The impact of dynamicallyincorporating IHG variations on MPC performance was evaluated over a one-month period. Results showed a 4 percent reduction in heatingenergy use and a 32 percent decrease in air quality discomfort, although thermal discomfort increased by 14 percent. These findings suggestthat in a multi-objective MPC framework, the accuracy of disturbance predictions, particularly those related to occupancy, can stronglyinfluence air quality comfort. They also emphasize the importance of carefully adjusting the weighting between energy consumption andthermal and air quality comfort, depending on the specific goals and priorities of each application.

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