Nayak, R.Jaidhar, C.D.2026-02-0620242024 17th International Conference on Security of Information and Networks, SIN 2024, 2024, Vol., , p. -https://doi.org/10.1109/SIN63213.2024.10871753https://idr.nitk.ac.in/handle/123456789/28766In terms of annual worldwide energy consumption, buildings use more energy than any other sector. Enhancing buildings' energy efficiency and ensuring security of the appliances requires iden-tifying abnormal power usage. Identifying anomalous power usage is essential for energy conservation. This study suggests an experimental analysis of SimDataset used for detecting micro-moment-based abnormal power usage. Five machine learning-based classifiers-Random Forest (RF), Support Vector Ma-chine (SVM), K Nearest Neighbors (KNN), Naive Bayes (NB), and Decision Tree (DT)-are used to detect unusual consumption of electricity. The Sim-Dataset has undergone binary and multi-class classi-fication. Effect on the performance of the classifiers after the inclusion of new features is examined. Computational complexity of the classifiers is also analyzed. Experimental results showed, the binary and multi-class classification using the RF model with the original dataset, with Min-Max Normalized Power feature and Appliance Id-based Normalized Power feature, produced identical and maximum accuracy, precision, recall, and F1-Score. © 2024 IEEE.Anomaly DetectionClassificationEnergy EfficiencyMachine LearningMicro-Moment