Unauthorized Power Usage Detection Using Gradient Boosting Classifier in Disaggregated Smart Meter Home Network

For the smart grid unauthorized power usage identification in a disaggregated smart meter network, this paper introduces different classification algorithms to detect anomalies in appliance consumption patterns. They are Logistic Regression(LG), Support Vector Machine (SVM), Decision Tree Classifier(DTC), Random Forest(RF), Ridge Regression Classifier(RRC) and Gradient Boosting Classifier(GBC). While most of the existing Machine Learning (ML) algorithms focuses on aggregated data in smart meter network or Advanced Metering Infrastructure(AMI). Our ML algorithms are based on load dis-aggregation, otherwise known as Non-Intrusive Load Appliance Monitoring (NIALM). This technique generates appliance-level power consumption data based on single, smart meter readings to improve detection performance and time complexity. Simulation results for preprocessed Almanac of Minutely Power dataset version 2 (AMPds2) demonstrate the reasonable improvement of the proposed machine learning model. Emphasis has been laid upon the practical application of the proposed ML to detect unauthorized power usage in AMI. GBC improves both detection and False positive rates (FPR) compared with other classifiers.