Electricity Theft Detection using Ensemble-Based Machine Learning Classifier

Power theft is still a major problem in the modern world, affecting the economy and the power system. It is mostly conducted using advanced techniques, such as illegal connection and meter tampering, that undermine the integrity of the power network and hinder attainable energy objectives. Machine learning methods based on ensemble learning, such as stacking classifiers, have been able to identify power theft. Improving accuracy and stability increases the efficiency of stacked classifiers to identify minor variations in electricity consumption patterns. It reduces false positives and increases the detection accuracy. In this research, for example, the Stacking classifier outperforms Decision Tree and Random Forest with an accuracy of 96%. It outperforms in terms of precision, recall, and F1-score for normal and theft classes, particularly in identifying unauthorised connection and meter tampering. Decision Tree and Random Forest classifiers give 94% and 88% accuracies, respectively, but are unable to classify the theft into different classes. From the above analysis, the Stacking classifier outperforms other classifiers in improving the accuracy in detection and overcoming some theft patterns. This research highlights how high-tech machine learning algorithms can be used to curb power theft while ensuring economic vitality, reliability and integrity of the World energy systems.