| Title |
Development and Data-Driven Evaluation ofInternal Heat Gain Prediction-Based MPC for HVAC Systems |
| Authors |
윤우승(Yun, Woo-Seung) ; 유원택(Ryu, Wontaek) ; 서현철(Seo, Hyuncheol) |
| DOI |
https://doi.org/10.5659/JAIK.2025.41.7.353 |
| Keywords |
Model Predictive Control; Internal Heat Gain; Building Energy Management |
| Abstract |
This study presents a method for continuously predicting internal heat gains (IHG) using plug and lighting power data, which are generally
more accessible than occupancy data. In addition, a procedure is introduced for effectively integrating these dynamic IHG predictions into a
multi-objective Model Predictive Control (MPC) framework. To reflect the dynamic behavior of IHG in real buildings, a Modelica simulation
environment was developed using long-term measured data on occupancy, plug loads, and lighting loads. The impact of dynamically
incorporating IHG variations on MPC performance was evaluated over a one-month period. Results showed a 4 percent reduction in heating
energy use and a 32 percent decrease in air quality discomfort, although thermal discomfort increased by 14 percent. These findings suggest
that in a multi-objective MPC framework, the accuracy of disturbance predictions, particularly those related to occupancy, can strongly
influence air quality comfort. They also emphasize the importance of carefully adjusting the weighting between energy consumption and
thermal and air quality comfort, depending on the specific goals and priorities of each application. |