Nontechnical Loss Detection of Electricity based on Neural Architecture Search in Distribution Power Networks

Electricity theft has always contributed a large portion of the nontechnical losses (NTLs) for the distribution networks, which usually causes severe concerns both on economics and safety of the power system operation. To cope with the rapid change of electricity theft methods, an auto NTLs detection system is proposed based on Neural Architecture Search (NAS) and Bayesian Optimization (BO). A case study utilizing NAS and BO has been performed on an electricity consumption dataset obtained from real customers of the State Grid Corporation of China. The auto-detection model achieved a F1 scores of 0.582 and an AUC of 0.919 which is competitive to the state-of-art artificial neural network.