When analyzing smart metering data, both reading errors and frauds can be identified. The purpose of this analysis is to alert the utility companies to suspicious consumption behavior that could be further investigated with on-site inspections or other methods. The use of Machine Learning (ML) algorithms to analyze consumption readings can lead to the identification of malfunctions, cyberattacks interrupting measurements, or physical tampering with smart meters. Fraud detection is one of the classical anomaly detection examples, as it is not easy to label consumption or transactional data. Furthermore, frauds differ in nature, and learning is not always possible. In this paper, we analyze large datasets of readings provided by smart meters installed in a trial study in Ireland by applying a hybrid approach. More precisely, we propose an unsupervised ML technique to detect anomalous values in the time series, establish a threshold for the percentage of anomalous readings from the total readings, and then label that time series as suspicious or not. Initially, we propose two types of algorithms for anomaly detection for unlabeled data: Spectral Residual-Convolutional Neural Network (SR-CNN) and an anomaly trained model based on martingales for determining variations in time-series data streams. Then, the Two-Class Boosted Decision Tree and Fisher Linear Discriminant analysis are applied on the previously processed dataset. By training the model, we obtain the required capabilities of detecting suspicious consumers proved by an accuracy of 90%, precision score of 0.875, and F1 score of 0.894.
Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance
