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Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system
Digital twin is regarded as the next-generation technology for the effective operation of heating, ventilation and air conditioning (HVAC) systems. It is essential to calibrate the digital twin models to match them closely with real physical systems. Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances. To address this challenge, this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems. This method utilizes clustering algorithms to remove redundant data for achieving data compression. Moreover, a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models. Its basic idea is that once a component model is calibrated by heuristic methods, its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components. By doing so, the calibration process can be guided, so that fewer iterations would be used. The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building. Results show that the proposed clustering compression-based method can reduce computation loads by 97%, compared to the conventional calibration method. And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6% of the time costs.
Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system
Digital twin is regarded as the next-generation technology for the effective operation of heating, ventilation and air conditioning (HVAC) systems. It is essential to calibrate the digital twin models to match them closely with real physical systems. Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances. To address this challenge, this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems. This method utilizes clustering algorithms to remove redundant data for achieving data compression. Moreover, a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models. Its basic idea is that once a component model is calibrated by heuristic methods, its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components. By doing so, the calibration process can be guided, so that fewer iterations would be used. The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building. Results show that the proposed clustering compression-based method can reduce computation loads by 97%, compared to the conventional calibration method. And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6% of the time costs.
Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system
Build. Simul.
Lu, Jie (Autor:in) / Tian, Xiangning (Autor:in) / Feng, Chenxin (Autor:in) / Zhang, Chaobo (Autor:in) / Zhao, Yang (Autor:in) / Zhang, Yiwen (Autor:in) / Wang, Zihao (Autor:in)
Building Simulation ; 16 ; 997-1012
01.06.2023
16 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
heating, ventilation and air conditioning systems , model calibration , digital twin , heuristic methods , clustering compression , hierarchical calibration Engineering , Building Construction and Design , Engineering Thermodynamics, Heat and Mass Transfer , Atmospheric Protection/Air Quality Control/Air Pollution , Monitoring/Environmental Analysis
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