Automatic Appliance Labeling for Unsupervised NILM Based on Hierarchical Decision-Making
Bo Liu, Weiqun Liu, Wenpeng Luan, Yixin Yu, Bochao Zhao, Yan Wang · IEEE Transactions on Instrumentation and Measurement · 2024
The unsupervised non-intrusive load monitoring (NILM) can determine fine-grained energy consumption information at the appliance level in unseen scenarios, by autonomously analyzing aggregate electricity data. However, a challenge for unsupervised NILM is to determine the actual physical names of discovered appliances, which hinders the large-scale implementation of NILM technologies. For addressing this problem, an automatic appliance labeling method is proposed in this paper, which can assign physical names to unknown appliances identified by unsupervised NILM. The general operation characteristics shared by same type of appliances are summarized. On this basis, a hierarchical decision-making process for appliance labeling is proposed. Rough set theory is adopted to select the potential candidate appliance set by evaluating general operation characteristics, followed by the most likely candidate name determination through voting on parametric characteristics. Furthermore, association pattern mining is carried out to discover recurring operation mode combinations and identify those associated modes corresponding to multi-mode appliances. Experimental results on real-measured and public datasets show that the proposed method can achieve accurate labeling of NILM results in various unseen scenarios with limited prior knowledge, which demonstrates the potential for practical application.