Methodology for Detecting Non-Technical Energy Losses Using an Ensemble of Machine Learning Algorithms

Non-technical losses (NTL) of electric power are a serious problem for electric distribution companies. The solution determines the cost, stability, reliability, and quality of the supplied electricity. The widespread use of advanced metering infrastructure (AMI) and Smart Grid allows all participants in the distribution grid to store and track electricity consumption. During the research, a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings. This model is an ensemble meta-algorithm (stacking) that generalizes the algorithms of random forest, LightGBM, and a homogeneous ensemble of artificial neural networks. The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample. Such a model, due to good accuracy indicators (ROC-AUC-0.88), can be used as a methodological basis for a decision support system, the purpose of which is to form a sample of suspected NTL sources. The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers, making them targeted and accurate, which should contribute to the fight against NTL and the sustainable development of the electric power industry.