Energy Theft Detection in Smart Grids Using Anomaly Detection Techniques with the UCI Smart Meter Dataset

Abstract

The study aimed to evaluate the effectiveness of Anomaly Detection techniques in detecting electrical power theft within Smart Grid environments by comparing the performance of Isolation Forest and Autoencoder models using the UCI Smart Meter Dataset. The study was based on an experimental approach, where the data underwent Data Preprocessing processes that included cleaning the data, processing missing values, normalizing the data, extracting properties, and then dividing it into training and testing data. The two unsupervised models were then trained and their performance evaluated using Accuracy, Precision, Recall, F1-Score, Specificity, and AUC-ROC indices, as well as ROC and Precision–Recall curves. The results showed that the Isolation Forest model outperformed the Autoencoder model across all performance indicators, achieving Accuracy of 96.8%, Precision of 83.1%, Recall of 81.9%, F1-Score of 82.5%, Specificity of 98.3%, and AUC-ROC of 99.0%, indicating its stronger performance in identifying abnormal consumption patterns associated with potential energy theft and reducing false alarm rates. The study recommends adopting the Isolation Forest model in Smart Grid monitoring systems, using more diverse datasets to improve the models' generalization ability, developing hybrid models to increase detection efficiency, and testing the models in real-world operating environments that support real-time monitoring.