Anomaly detection and prediction of energy consumption for smart homes using machine learning

Abstract

As technology advances, smart homes are being increasingly adopted, thus generating massive data that pose new research challenges. We propose a machine learning framework for monitoring energy consumption in smart home devices. The proposed framework involves an anomaly detection module, followed by a predictive model to forecast energy consumption patterns in a typical smart home. We employ three outlier-based techniques for anomaly detection: (1) local outlier factor, (2) connectivity-based outlier factor, and (3) cluster-based local outlier factor. Furthermore, we apply random forest, linear regression, decision tree, and the ensemble techniques of adaptive, gradient, and extreme gradient boosting to anomaly free data to develop baseline models that predict the energy consumption patterns of smart home devices. The framework is evaluated on three publicly available energy datasets collected from various smart homes. The experimental results reveal that the cluster-based local outlier factor with extreme gradient boosting achieves promising results with high prediction accuracy.