SmartFD: A Real Big Data Application for Electrical Fraud Detection

Abstract

The main objective of this paper is the application of big data analytics to a real case in the field of smart electric networks. Smart meters are not only elements to measure consumption, but they also con stitute a network of millions of sensors in the electricity network. These sensors provide a huge amount of data that, once analyzed, can lead to significant advances for the society. In this way, tools are being developed in order to reach certain goals, such as obtaining a better consumption estimation (which would imply a better production planning), finding better rates based on the time discrimination or the contracted power, or minimizing the non-technical losses in the network, whose actual costs are eventually paid by end-consumers, among others. In this work, real data from Spanish consumers have been analyzed to detect fraud in con sumption. First, 1 TB of raw data was preprocessed in a HDFS-Spark infrastructure. Second, data duplication and outliers were removed, and missing values handled with specific big data algorithms. Third, cus tomers were characterized by means of clustering techniques in different scenarios. Finally, several key factors in fraud consumption were found. Very promising results were achieved, verging on 80% accuracy