Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection

In order to improve the efficiency of electricity theft detection, the power theft detection area and users should be better integrated, we proposed a Blending ensemble learning electricity theft detection model based on the Base Learner Selection Strategy (BLSS). Firstly, the adaptive synthetic (ADASYN) sampling method is used to process the unbalanced power consumption data, and the sample distribution of training data is balanced. Secondly, the BLSS selection method is used to screen the optimal base learner combination and construct the Blending ensemble learning model. Then, based on the historical data, the model makes a short-term prediction of the power consumption of the station area the next day, and focuses on the verification of the suspected energy-stealing station area where the Root Mean Square Percentage Error (RSPE) exceeds the threshold, so as to lock in the potential energy stealing users. Finally, through the comparison and verification of real examples, the search scope for electricity theft inspections was reduced by 79.17%, greatly improving the detection efficiency of the power supply company. At the same time, the model’s electricity theft detection and recognition accuracy rate can be as high as 97.50%. The Blending ensemble learning electricity stealing detection model based on the BLSS base learner selection method has strong electricity stealing detection and recognition ability.