Energy efficiency is a big concern in industrial sectors. Finding the root cause of anomaly state of energy efficiency can help to improve energy efficiency of industrial systems and therefore save energy cost. In this research, we propose to use transfer entropy (TE) for root cause analysis on energy efficiency of industrial systems. A method, called TE flow, is proposed in that a TE flow from physical measurements of each subsystem to the energy efficiency indicator along timeline is considered as causal strength for diagnosing root cause of anomaly states of energy efficiency of a system. The copula entropy-based nonparametric TE estimator is used in the proposed method. We conducted experiments on real data collected from a compressing air system to verify the proposed method. Experimental results show that the TE flow method successfully identified the root cause of the energy (in)efficiency of the system.

翻译：能源效率是工业领域的一个重要关注点。发现能效异常状态的根因有助于提升工业系统的能效，从而节约能源成本。本研究提出使用传递熵（transfer entropy, TE）对工业系统的能效进行根因分析。我们提出了一种称为TE流（TE flow）的方法，该方法将每个子系统的物理测量值沿时间轴到能效指标的TE流视为因果强度，用于诊断系统能效异常状态的根因。在所提方法中，采用了基于copula熵的非参数传递熵估计器。我们利用从压缩空气系统采集的真实数据进行了实验，以验证所提方法。实验结果表明，TE流方法成功识别了系统能效（低效）的根因。