Non-Intrusive Load Identification Based on Multivariate Features and Information Entropy-Weighted Ensemble

In non-intrusive load monitoring (NILM), single-dimensional features exhibit limited representational capacity, while feature fusion at the feature layer often leads to information loss due to dimensional transformation, as well as the risk of dimensional explosion caused by the newly added features. To address these challenges, this paper proposes a non-intrusive load identification method based on multivariate features and information entropy-weighted ensemble. Specifically, one-dimensional numerical features related to power and current are input into traditional machine learning models, and two-dimensional image features of binary V-I trajectory are processed by the deep neural network model Swin Transformer. Information entropy is employed to adaptively determine the weight of each classification model, and a weighted voting strategy is utilized to combine the decisions of multiple models to obtain the final identification result. This approach achieves feature fusion at the decision layer, effectively avoiding dimensional transformations and fully leveraging the complementary advantages of features from different dimensions. Experimental results show that the proposed method achieves identification accuracies of 99.48% and 99.54% on the public datasets PLAID and WHITED, respectively.