Fraud Detection for Metered Costumers in Power Distribution Companies Using C5.0 Decision Tree Algorithm

Abstract

Non-technical Losses mainly Electricity theft has been a main concern for power utilities from last many years. Power utilities are estimated to lose billion dollars annually because of illegal usage of electricity by fraudulent consumers. Researchers are trying different methods for proficiently recognizing fraudster costumers. This research suggests a new approach based on C5 algorithm for efficiently identifying consumers involved in electricity theft. The C5.0 algorithm is a modified form of the C4.5 algorithm. It is also one of the decision tree algorithms but with a much-improved classification rate. The C5.0 algorithm relies on monthly energy consumption data to identify any anomaly in consumer energy usage data associated with NTL behavior. There are many types of fraud committed by fraudulent consumers but this research is focused on fraudulent consumers who have a unexpected deviation from their usual load profile. The motivation of this research is to aid Power distribution companies in Pakistan to decrease there NTL’s due to pilfering in energy consumption by fraudulent consumers. The accuracy of the C5.0 algorithm is 94.61% which is much higher when compared to some state of the art machine learning algorithms like Random forest, Support Vector Machine, K-NN and other decision trees.