  In order to keep track of the operational state of power grid, the world's
largest sensor systems, smart grid, was built by deploying hundreds of millions
of smart meters. Such system makes it possible to discover and make quick
response to any hidden threat to the entire power grid. Non-technical losses
(NTLs) have always been a major concern for its consequent security risks as
well as immeasurable revenue loss. However, various causes of NTL may have
different characteristics reflected in the data. Accurately capturing these
anomalies faced with such large scale of collected data records is rather
tricky as a result. In this paper, we proposed a new methodology of detecting
abnormal electricity consumptions. We did a transformation of the collected
time-series data which turns it into an image representation that could well
reflect users' relatively long term consumption behaviors. Inspired by the
excellent neural network architecture used for objective detection in computer
vision domain, we designed our deep learning model that takes the transformed
images as input and yields joint featured inferred from the multiple aspects
the input provides. Considering the limited labeled samples, especially the
abnormal ones, we used our model in a semi-supervised fashion that is brought
out in recent years. The model is tested on samples which are verified by
on-field inspections and our method showed significant improvement.
