暖通空调
冷冻机
空调
冷水机组
均方误差
计算机科学
算法
功能(生物学)
模拟
工程类
数学
统计
机械工程
制冷剂
物理
气体压缩机
热力学
生物
进化生物学
作者
Kang Chen,Xu Zhu,Burkay Anduv,Xinqiao Jin,Zhimin Du
出处
期刊:Energy
[Elsevier]
日期:2022-07-01
卷期号:251: 124040-124040
被引量:21
标识
DOI:10.1016/j.energy.2022.124040
摘要
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 (R2) 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 R2 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI