Fusing V-I Trajectory and Statistical Features for Improved Non-Intrusive Appliance Identification

Non-intrusive load monitoring (NILM) offers a practical solution for intelligent energy management in consumer electronics by estimating fine-grained energy consumption information for individual appliances from aggregated power signals measured at limited locations. With the rapid increase in appliance brands and types, constructing unique load signatures and establishing reliable appliance identification methods become progressively crucial for NILM. Deep neural networks have proven effective feature extraction capabilities and excellent identification performance in different fields. The primary approach in the current literature to extend this trend involves expanding the size of networks. However, this leads to rapidly increasing computational costs, with only marginal improvements in performance. In this paper, we propose and compare three different strategies for the fusion of the V-I trajectory and statistical features in an end-to-end trainable manner, evaluating feature fusion at various stages within the model (Feature-Level Fusion and Decision-Level Fusion), which automatically learn jointly from V-I features and a set of manually defined statistical features. As a result, the proposed approach improves cost efficiency while enhancing appliance identification performance. Experiments on diverse benchmark datasets demonstrate that the proposed feature fusion models cost-effectively achieve cutting-edge appliance identification performance.