  Electricity theft is a major problem around the world in both developed and
developing countries and may range up to 40% of the total electricity
distributed. More generally, electricity theft belongs to non-technical losses
(NTL), which are losses that occur during the distribution of electricity in
power grids. In this paper, we build features from the neighborhood of
customers. We first split the area in which the customers are located into
grids of different sizes. For each grid cell we then compute the proportion of
inspected customers and the proportion of NTL found among the inspected
customers. We then analyze the distributions of features generated and show why
they are useful to predict NTL. In addition, we compute features from the
consumption time series of customers. We also use master data features of
customers, such as their customer class and voltage of their connection. We
compute these features for a Big Data base of 31M meter readings, 700K
customers and 400K inspection results. We then use these features to train four
machine learning algorithms that are particularly suitable for Big Data sets
because of their parallelizable structure: logistic regression, k-nearest
neighbors, linear support vector machine and random forest. Using the
neighborhood features instead of only analyzing the time series has resulted in
appreciable results for Big Data sets for varying NTL proportions of 1%-90%.
This work can therefore be deployed to a wide range of different regions around
the world.
