  Electricity theft, the behavior that involves users conducting illegal
operations on electrical meters to avoid individual electricity bills, is a
common phenomenon in the developing countries. Considering its harmfulness to
both power grids and the public, several mechanized methods have been developed
to automatically recognize electricity-theft behaviors. However, these methods,
which mainly assess users' electricity usage records, can be insufficient due
to the diversity of theft tactics and the irregularity of user behaviors.
  In this paper, we propose to recognize electricity-theft behavior via
multi-source data. In addition to users' electricity usage records, we analyze
user behaviors by means of regional factors (non-technical loss) and climatic
factors (temperature) in the corresponding transformer area. By conducting
analytical experiments, we unearth several interesting patterns: for instance,
electricity thieves are likely to consume much more electrical power than
normal users, especially under extremely high or low temperatures. Motivated by
these empirical observations, we further design a novel hierarchical framework
for identifying electricity thieves. Experimental results based on a real-world
dataset demonstrate that our proposed model can achieve the best performance in
electricity-theft detection (e.g., at least +3.0% in terms of F0.5) compared
with several baselines. Last but not least, our work has been applied by the
State Grid of China and used to successfully catch electricity thieves in
Hangzhou with a precision of 15% (an improvement form 0% attained by several
other models the company employed) during monthly on-site investigation.
