An adaptive appliance identification method leveraging topological data analysis

Non-intrusive load monitoring (NILM) is a technique that involves analyzing changes in voltage and current flowing through the main feeder to determine which appliances are in operation and their energy consumption. With the increasing amount and diversity of electric loads nowadays, it is becoming increasingly important to extract unique load signatures and build robust classification models for NILM. Topological data analysis (TDA) studies the properties of space that are preserved under continuous deformations, which can reveal the structure and relationships within complex datasets, such as networks, graphs, and manifolds. In this paper, we use TDA as feature extractor to mine vast of non-linear shape features from Voltage–Current (V-I) trajectory for appliance identification. Then, an adaptive feature selection method based on mutual information (MI) is proposed, as a result, the selected subset of features is more discriminative for the adopted dataset. Further, using selected features, the strategy of Frienemy Indecision Region Dynamic Ensemble Selection (FIRE-DES), which adaptively selects combination of classifiers according to data-samples by considering the improvement by indecision region, is employed for non-intrusive appliance classification. By such fusion of TDA and technique of ensemble learning, the experimental results on two public datasets prove efficacy of proposed method in both identification accuracy and computation time.