@article{LIU2026, 
author = {Xuefeng LIU and Wengjing MA},
title = {Multi-Variable Interaction and Parameter Identification of the Global Coupling Mechanism Model for Central Air Conditioning},
year = {2026},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {54},
number = {7},
pages = {105-118},
keywords = {central air conditioning cooling system, sensitivity analysis, analysis of variance, multi-objective optimization, parameter identification},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.250394},
doi = {10.12141/j.issn.1000-565X.250394},
abstract = {In the long-term operation of the mechanistic model of a central air conditioning system, key physical parameters may drift due to practical factors such as pipe scaling and equipment aging. this leads to a gradual decline in model accuracy and severely affecting the performance of model-based optimal control strategies. To ensure the long-term reliability of the model and the effectiveness of the control strategy, it is essential to conduct an in-depth performance analysis of the constructed physical model and perform effective parameter identification based on actual operational data. Building upon the mechanistic model of the central air conditioning system, a sensitivity analysis is performed to extract a set of key parameters that significantly influence system performance. An optimization function is established, aiming to minimize the total error of target variables such as chilled water inlet and outlet temperatures and energy efficiency ratio (EER). The parameter identification problem is transformed into an optimization problem, which is solved using an approximate optimization method. The identification method is validated using actual engineering data. Results demonstrate that this method significantly reduces errors in target variables. Taking EER as an example, before identification, the mean absolute error (MAE) is 1.93, the root mean square error (RMSE) is 1.98, and the Mean absolute percentage error (MAPE) reaches as high as 61.4%. After identification, under single-target identification, the MAE drops to 0.008, RMSE to 0.004, and MAPE to 0.29%. Under multi-target identification, the MAE decreases to 0.28, RMSE to 0.51, and MAPE to 9.74%. The validation results confirm the method’s effectiveness in improving model accuracy and engineering applicability.}
}