Adaptive Energy Theft Detection in Smart Grids Using Self-Learning With Dual Neural Network

Energy theft is an extremely prominent challenge causing significant energy and revenue losses for utility providers worldwide. The introduction of advanced metering infrastructures consisting of smart meter deployments has undeniably extended the attack surface, enabling individual consumers or prosumers to trigger composite energy theft attack vectors. In this work, we introduce an energy theft detection system capable of distinguishing properties of power consumption and generation theft with possible misconfigurations caused by nonmalicious intent. The proposed approach is adaptive through a self-learning operation that is updated continuously as new measurements become available. With the synergistic use of measurements collected by real PV installations and openly available weather information, the system achieves high accuracy and precision result in theft identification over streamed data measurements. Thus, it promotes low computational costs and its architecture can be easily integrated within smart grid infrastructures to realize next-generation cross-batch energy theft detection.