HyMO-RF: Automatic Hyperparameter Tuning for Energy Theft Detection Based on Random Forest Classification

To curb energy theft and identify fraudsters and other non-technical losses, power distribution companies have used Machine Learning algorithms and a large amount of data with high granularity from electricity consumption units. Those data are collected from consumers through Advanced Metering Infrastructure (AMI) like Smart Meters (SM) being remotely collected in real time and several samples per day. In emerging countries like Brazil, most energy meters are technologically limited or electromechanical. Those devices can measure only aggregated values of energy consumption in a monthly basis. This work proposes HyMO-RF, one strategy of using a multi-objective search algorithm to improve the performance of a machine-learning model in the detection energy theft in a scenario of limited resources (AMI and SM). The proposed approach in this paper (HyMO-RF) is based on hyperparameter tuning and the main contribution is to associate multi-objective algorithms to determine an optimized combination of hyperparameters in order to maximize the classification model’s performance. A real life corporate dataset was provided by a Brazilian power distribution company CPFL ENERGIA™. The data was properly anonymized and securely stored. We used NSGAII multi-objective algorithm for hyperparameters tuning, improving the Random Forest (RF) classifier’s performance. Results achieved in terms of Precision and F1-score metrics were 0.83 and 0.73 respectively. An additional field study showed that the solution proposed already impacted the operation of the fraud detection team in a positive way by having an accuracy of 74% of the suspect consumer units inspected.