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

Yue Liu (), Wenxia You and Miao Yang
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Yue Liu: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Wenxia You: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Miao Yang: Hubei Qingjiang Hydropower Dev Co., Ltd., Yichang 443000, China

Energies, 2025, vol. 18, issue 9, 1-27

Abstract: 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.

Keywords: NILM; multivariate features; information entropy-weighted voting; V-I trajectory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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