Comparative Study of Data Driven Approaches Towards Efficient Electricity Theft Detection in Micro Grids

In this research article, we tackle the following limitations: high misclassification rate, low detection rate and, class imbalance problem and no availability of malicious or theft samples. The class imbalanced problem is severe issue in electricity theft detection that affects the performance of supervised learning methods. We exploit the adaptive synthetic minority oversampling technique to tackle this problem. Moreover, theft samples are created from benign samples and we argue that the goal of theft is to report less than consumption actual electricity consumption. Different machine learning and deep learning methods including recently developed light and extreme gradient boosting (XGBoost), are trained and evaluated on a realistic electricity consumption dataset that is provided by an electric utility in Pakistan. The consumers in the dataset belong to different demographics and, different social and financial backgrounds. Different number of classifiers are trained on acquired data; however, long short-term memory (LSTM) and XGBoost attain high performance and outperform all classifiers. The XGBoost achieves a 0.981 detection rate and 0.015 misclassification rate. Whereas, LSTM attains 0.976 and 0.033 detection and misclassification rate, respectively. Moreover, the performance of all implemented classifiers is evaluated through precision, recall, F1-score, etc.