Decision Tree based Electricity Theft Detection in Smart Grid
Soroush Omidvar Tehrani, Mohammad Hossein Yaghmaee, Mohsen Asadi
Abstract
One aspect of using smart meters is detecting anomalies in advanced metering infrastructure. Electricity theft, as a well-known anomaly, can be discovered by various machine learning algorithms. In this paper, decision tree, random forest, and gradient boosting methods are implemented and performed on collected power consumption data from 114 single-family apartments to detect non-technical loss, which are generated by various scenarios. Performances of these algorithms are analyzed with and without clustering on the measured data of each user.
Topics & Concepts
Decision treeSmart gridGradient boostingMetering modeComputer scienceRandom forestCluster analysisElectricityBoosting (machine learning)Anomaly detectionTree (set theory)Power consumptionData miningSmart meterMachine learningArtificial intelligencePower (physics)EngineeringQuantum mechanicsMechanical engineeringElectrical engineeringMathematical analysisPhysicsMathematicsElectricity Theft Detection TechniquesSmart Grid Security and ResilienceWater Systems and Optimization