An evolving learning method —growing Gaussian mixture regression—for modeling passive chilled beam systems in buildings
An evolving learning method —growing Gaussian mixture regression—for modeling passive chilled beam systems in buildings
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DOI：
10.1016/j.enbuild.2022.112227
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发表时间：
2022-05
影响因子：
6.7
通讯作者：
Liping Wang;James Braun;Sujit Dahal
中科院分区：
文献类型：
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作者：
Liping Wang;James Braun;Sujit Dahal
尽管冷冻梁系统被认为是性能最好的暖通空调系统之一，但很少有研究着眼于冷冻梁系统的基于数据驱动的建模。此外，还没有基于学习的数据驱动方法应用于建筑物中的冷冻梁系统。在这项研究中，我们使用一种进化学习方法，生长高斯混合回归(GGMR)，来预测被动冷冻梁(PCB)系统的冷却速度，其中训练、进化和验证是使用来自实际系统测量和建筑能源模拟的数据进行的。GGMR更新高斯分量的权重系数、均值和协方差矩阵等关键参数，以适应系统运行中训练数据以外的变化。实例研究表明，GGMR是一种有效的基于进化学习的数据驱动方法，可以准确预测印刷电路板系统的冷却速度。文中讨论了GGMR模型的关键性能参数的选择，包括组件数量、训练数据量和学习率。建议进一步探索GGMR模型，以预测其他复杂暖通空调系统的性能，如辐射板或混合模式通风系统。
Although chilled beam systems are considered as one of the best performing HVAC systems, few studies have looked into data-driven-based modeling for chilled beam systems. Furthermore, no learning-based data-driven methods have been applied to chilled beam systems in buildings. In this study, we used an evolving learning method, growing Gaussian mixture regression (GGMR), to predict cooling rates for passive chilled beam (PCB) systems where the training, evolution, and validation were carried out using data from real system measurements and from building energy simulation. GGMR updates key parameters such as weight coefficients, means, and covariance matrices of Gaussian components to adapt to changes in system operation beyond training data. This case study demonstrated that GGMR is an effective evolving learning-based data-driven method for accurately predicting cooling rates of PCB systems. The selection of key performance parameters of GGMR models including the number of components, training data size, and the learning rate was discussed in this paper. It is recommended that GGMR models could be further explored for predicting the performance of other complex HVAC systems such as radiant slab or mixed-mode ventilation systems.