Electricity Theft Detection using Empirical Mode Decomposition and K-Nearest Neighbors

Electricity theft is a criminal practice of stealing electricity. In the country like Pakistan where the consumption is more than the production, the electricity theft can be hazardous for the economy. During the year 2017-18, there was a loss of 53 billion Rs. to the economy due to electricity theft. A novel system for the detection of electricity thefts is designed. The dataset provided by State Grid Corporation of China (SGCC) was used which contained two classes i.e. normal and theft. The dataset comprised of data collected for 1,035 days. The dataset included various missing and erroneous values. Preprocessing techniques such as interpolation was used to get the missing values and for the breakdown of signal, empirical mode decomposition was employed. After that the features were extracted from the signals of both classes. After a number of experiments combinations of features were found that gave maximum accuracy. K-nearest neighbors (KNN) classifier was used because of its advantage that it is very fast and simple. System was able to detect the electricity theft with accuracy of 91.0%. The system is very reliable and can be helpful in reducing the losses due to electricity theft. It is a very easy to use system as well as cost efficient.