Non-Intrusive Load Monitoring Based on Feature Extraction of Change-point and XGBoost Classifier

Refined measurement of electricity consumption information on the residential side is an important foundation for achieving demand-side management and promoting smart grid construction. In order to obtain highly accurate appliancelevel energy consumption data, this paper proposes a load identification method based on feature extraction of change-point and XGBoost classifier. Firstly, the event detection is performed by using secondary window determination to determine whether an event has occurred based on changes in power statistics; then, according to the detection results, comprehensively extract the transient-state and steady-state features of the electrical signal in the window; lastly, it is input into the XGBoost model for load energy decomposition. In the case validation phase, the PLAID dataset is compared with five benchmark algorithms, and the results show the effectiveness and superiority of the method proposed in this paper for resident-side load identification.