Detecting abnormal electricity usage using unsupervised learning model in unlabeled data

Abstract

Smart-home systems achieved great popularity in the last decade as they increase the comfort and quality of life. Reduction of energy consumption became a very important desiderate in the context of the explosive technological development of modern society with a major impact on the future development of mankind. Moreover, due to the large amount of data available from smart meters installed in households. It makes leverage to able to find data abnormalities for better monitoring and forecasting. Detecting data anomalies helps in making a better decision for reducing energy usage wasted. In recent years, machine learning models are widely used for developing intelligent systems. Currently, researchers' main focus is on developing supervised learning models for predicting anomalies. However, there are challenges to train models with unlabeled data indicating data anomaly or not. In this paper, abnormalities are detected in electricity usage using unsupervised learning and evaluated using Excess Mass. The unsupervised anomaly detection model is based on Gaussian Mixture Model (GMM) and Isolation Forest (iForest). The models are compared with Local Outlier Factor (LOF) and One-class support vector machine (OCSVM). The proposed framework is tested with actual electricity usage and temperature data obtained from Numenta Anomaly Benchmark (NAB), which contains normal and anomaly data in time series. Finally, it has been observed that the iForest out-performed as the detection model for the selected use case. The outcome showed that the iForest can quickly detect anomalies in electricity usage data with only a sequence of data without feature extraction. The proposed model is suitable for the Smart Home Energy Management System's practical requirement and can be implemented in various houses independently. The proposed system can also be extended with the various use cases having similar data types.