Pu, Jiaxuan;
Li, Peitao;
Wang, Zhaoying;
Wang, Yaran;
Jiang, Yan;
Zhang, Huan;
Yang, Lanxiang;
... Zhang, Bin; + view all
(2025)
A novel high-fidelity dual-scale model predictive control strategy for energy-efficient VAV systems: Application to metro station platforms.
Sustainable Energy Technologies and Assessments
, 83
, Article 104663. 10.1016/j.seta.2025.104663.
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Abstract
To improve energy efficiency and temperature control accuracy in metro stations, this study proposes a novel high-fidelity dual-scale model predictive control (HFDS-MPC) strategy for variable air volume (VAV) systems. The core innovation lies in coupling dual-scale spatial grids with a Kalman filter-based estimator to achieve real-time correction of model predictions. The HFDS-MPC strategy uses spatial correlations in temperature fields to estimate full-grid temperatures from limited sensors, greatly reducing sensor deployment. Its high-fidelity modeling of process error covariance, derived from large-scale temperature simulations, ensures accurate state estimation within the Kalman filter. The key influencing factors and energy saving potential of the control strategy are analyzed. Results show that the prediction horizon is the most influential parameter, with an optimal value improving control accuracy by 61.2 %. Under various disturbances, the HFDS-MPC strategy maintains platform temperature within 4.4 % of the reference. Applying the proposed control strategy to the variable air volume system of a metro station can effectively enhance thermal comfort and decrease air conditioning energy consumption by 28.8 %, saving annual electricity costs by 21,177 CNY in comparison to constant air volume control.
| Type: | Article |
|---|---|
| Title: | A novel high-fidelity dual-scale model predictive control strategy for energy-efficient VAV systems: Application to metro station platforms |
| DOI: | 10.1016/j.seta.2025.104663 |
| Publisher version: | https://doi.org/10.1016/j.seta.2025.104663 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216886 |
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