Artificial Intelligence Methods for Anomaly Detection in Energy Consumption

Non-technical losses represent a significant challenge for energy distribution companies due to their economic impact. These losses typically arise from irregularities at supply points and fraudulent customer behavior. In Cuba, electricity meter readings are performed manually, and consumption data is processed using spreadsheets that combine weighted criteria to generate alerts for potential anomalies. This procedure is prone to vulnerabilities such as manual data entry errors, incorrect key assignments, and human mistakes made by field readers, which compromise the reliability of the analysis. In this paper we present an anomaly detection system for electricity consumption, developed using artificial intelligence techniques to identify irregularities based on monthly reports from residential users. Various machine learning methods were evaluated, with eXtreme Gradient Boosting (XGBoost) standing out for its effectiveness in handling imbalanced datasets. Additionally, a web application was implemented using Flask to process consumption data and provide real-time predictions, optimizing the management of nontechnical losses. The results confirm that, with real consumption data, the algorithms achieve high accuracy in fraud detection, even in scenarios with severe class imbalance, validating the robustness of the system. Beyond its application in the energy sector, this solution is adaptable to other contexts requiring anomaly detection in transactional data. This proposal contributes to reducing economic losses, improving operational efficiency, and laying the groundwork for future research in intelligent monitoring systems.