A Feature Engineering-Based NILM Framework for Appliance Recognition Considering Data Class Imbalance

Due to the diversity and randomness of residential consumption behavior, as well as the different share of domestic electricity demand by appliances, class imbalance problems exist in the non-intrusive load monitoring (NILM) system. The recognition model becomes biased toward the majority class, which makes the recognition of the minority class difficult. To tackle this challenge, we propose a novel NILM framework for appliance recognition to overcome the insufficient learning problems of the minority class, which combines a multi-domain feature extraction module, a two-stage feature selection, and a data oversampling method. Multiple features are extracted to make full of the complementarity of combined features to enhance appliance recognition. Furthermore, a two-stage feature selection based on minimal-redundancy-maximal-relevance (mRMR) and random forest (RF) is developed to select an optimal feature set. Subsequently, an oversampling method combining K-medoids and the synthetic minority oversampling technique (KM-SMOTE) with sampling weights is proposed for data augmentation of the minority class to handle imbalance learning problems. The effectiveness of the proposed method is verified by the experiments on the public dataset PLAID. The results show that, compared with the state-of-the-art techniques, the overall recognition accuracy and F1-score with the proposed method are enhanced by 4.61% and 2.04%, respectively.