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In order to keep track of the operational state of power grid, the world'slargest sensor systems, smart grid, was built by deploying hundreds of millionsof smart meters. Such system makes it possible to discover and make quickresponse to any hidden threat to the entire power grid. Non-technical losses(NTLs) have always been a major concern for its consequent security risks aswell as immeasurable revenue loss. However, various causes of NTL may havedifferent characteristics reflected in the data. Accurately capturing theseanomalies faced with such large scale of collected data records is rathertricky as a result. In this paper, we proposed a new methodology of detectingabnormal electricity consumptions. We did a transformation of the collectedtime-series data which turns it into an image representation that could wellreflect users' relatively long term consumption behaviors. Inspired by theexcellent neural network architecture used for objective detection in computervision domain, we designed our deep learning model that takes the transformedimages as input and yields joint featured inferred from the multiple aspectsthe input provides. Considering the limited labeled samples, especially theabnormal ones, we used our model in a semi-supervised fashion that is broughtout in recent years. The model is tested on samples which are verified byon-field inspections and our method showed significant improvement.

Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning