Detecting energy theft with partially observed anomalies

Energy theft poses a significant threat to the power industry, causing financial losses and grid instability. Existing detection methods often struggle with limited labeled data and the emergence of new, unobserved theft patterns. To address these challenges, we propose a novel method for energy theft detection that effectively leverages both partially observed anomalies and unlabeled data. Our approach integrates Discrete Wavelet Transform (DWT) for feature extraction, Fuzzy C-Means clustering for anomaly grouping, and weighted multi-class logistic regression for ensemble learning. Extensive experiments on a realistic dataset demonstrate that our method achieves high detection accuracy, outperforming several state-of-the-art methods, including deep learning models, while maintaining significantly lower computational cost. This robust and efficient approach enables effective detection of unobserved anomaly classes and reduces false positives, making it a valuable tool for developing reliable energy theft detection systems. We further conduct a feature importance analysis to identify influential features for optimizing detection accuracy and efficiency.