Technique of Feature Extraction Based on Interpretation Analysis for Multilabel Learning in Nonintrusive Load Monitoring With Multiappliance Circumstances

Nonintrusive load monitoring (NILM) aims to analyze the aggregate information of power consumption and recognize the separate operation states of each individual electrical appliance, in which methods of machine learning are frequently used for efficient and effective computation. This article employs multilabel learning models as the main technique to measure the NILM problems with multiple electrical appliances. To precisely extract the information among the aggregate dataset and obtain better effects of data modeling, feature extraction based on interpretation analysis is carried out along of model training. Meanwhile, swapping the order of input labels is also implemented to further characterize the interrelationships and influences among the labels themselves during the modeling process. The results of simulation show that the feature extraction could improve the model performance as it may mitigate the mutual interference among input features and find crucial information to the target electrical appliances. Also, different sort of labels about the operation states of appliances would have impact on the model performance, on both individual and global predictions.