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A semi-supervised approach for electricity theft monitoring: A use case in Cyprus

Electricity theft constitutes one of the main reasons of non-technical electricity losses, which is able to cause significant economic losses and harmfulness to power grid suppliers. During the last decade, the data analysis on smart meter and sensor data has revolutionized electricity theft monitoring. In this work, we propose a new complete framework for the online detection of electricity theft in Cyprus, which is based on the predictions of a deep learning model. A significant advantage of the proposed model is that it does not require any prior knowledge of any electricity theft consumer during the training phase; to mitigate the lack of labels in our data. The reported numerical experiments on a labeled dataset from another demo site, demonstrate empirical evidence about the robustness of the developed model. Finally, we provide a detailed presentation of the model’s integration and deployment through state-of-the-art model serving tools.