Journal
ENERGY
Volume 251, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.124040
Keywords
Digital twins; Broad learning system; Incremental learning; Online model updating; HVAC system
Categories
Funding
- National Natural Science Foundation of China [51876119]
- National Key R&D Program of China [2021YFE0107400]
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This paper proposes an intelligent digital twin framework for HVAC systems and presents a broad learning system (BLS) to build the simulation layer of the chiller and its digital twin platform. Experimental results show that the proposed method has better prediction precision and can be updated in real-time within a shorter time.
Digital Twins (DT) can be used for the energy efficiency management of entire life cycle of HVAC systems. The existing chiller models usually can not update in real-time, so they are not suitable for real-time interactions between DT models and real physical systems. In this paper, an intelligent DT framework is proposed for HVAC systems, which includes the equipment, data, simulation, and application layers. Broad learning system (BLS) is presented to build the simulation layer of the chiller and its DT platform. The basic BLS model is optimized to reach the best performance by choosing linear rectification function as activation function and setting batch size to 64 by enumeration method. The real HVAC system located in Zhejiang province is selected to verify the proposed method. For the first half year operation, the average mean absolute error, root mean square error and coefficient of determination (R-2) of Multi-BLS model for nine chillers can reach 9.04,15.20 and 0.98 respectively. For the second half year operation, the proposed method can be updated in 4.63s and its R-2 is 0.95. Compared with conventional models, the proposed Multi-BLS model has better prediction precision and can be updated in real-time within a shorter time.(C) 2022 Elsevier Ltd. All rights reserved.
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