期刊
ENERGIES
卷 14, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/en14071996
关键词
model predictive control (MPC); railway passenger station; radiant floor heating; hybrid air conditioning system; RC model
资金
- National Key R&D Program of China [2018YFC0705000]
This study proposes an adaptive building model-based model predictive control (MPC) system to address the heating control challenges of a railway station on the Tibetan Plateau. Results show that the adaptive MPC improves indoor thermal comfort, reduces energy consumption by 22.2% compared to PID control, and achieves 11.5% energy savings compared to MPC without adaptive model.
A model predictive control (MPC) system with an adaptive building model based on thermal-electrical analogy for the hybrid air conditioning system using the radiant floor and all-air system for heating is proposed in this paper to solve the heating supply control difficulties of the railway station on Tibetan Plateau. The MPC controller applies an off-line method of updating the building model to improve the accuracy of predicting indoor conditions. The control performance of the adaptive MPC is compared with the proportional-integral-derivative (PID) control, as well as an MPC without adaptive model through simulation constructed based on a TRNSYS-MATLAB co-simulation testbed. The results show that the implementation of the adaptive MPC can improve indoor thermal comfort and reduce 22.2% energy consumption compared to the PID control. Compared to the MPC without adaptive model, the adaptive MPC achieves fewer violations of constraints and reduces energy consumption by 11.5% through periodic model updating. This study focuses on the design of a control system to maintain indoor thermal comfort and improve system efficiency. The proposed method could also be applied in other public buildings.
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