LAUSR

Abstract Advanced metering infrastructure allows the two-way sharing of information between smart meters and utilities. However, it makes smart grids more vulnerable to cyber-security threats such as energy theft. This study suggests ensemble machine learning (ML) models for the detection of energy theft in smart grids using customers’ consumption patterns. Ensemble ML models are meta-algorithms that create a pool of several ML approaches and combine them smartly into one predictive model to reduce variance and bias. A number of algorithms, including adaptive boosting, categorical boosting, extreme-boosting, light boosting, random forest, and extra trees, were tested to find their false positive and detection rates. A data pre-processing method was employed to improve detection performance. The statistical approach of minority over-sampling was also employed to tackle over-fitting. An extensive analysis based on a practical dataset of 5000 customers reveals that bagging models outperform other algorithms. The random forest and extra trees models achieve the highest area under the curve score of 0.90. The precision analysis shows that the proposed bagging methods perform better.