A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network

Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to detect anomalies associated with electricity theft in the AMI system, based on a combination of two robust machine learning algorithms; K-means and Deep Neural Network (DNN). K-means unsupervised machine learning algorithm is used to identify groups of customers with similar electricity consumption patterns to understand different types of normal behavior. DNN algorithm is used to build an accurate anomaly detection model capable of detecting changes or anomalies in usage behavior and deciding whether the customer has a normal or malicious consumption behavior. The proposed model is constructed and evaluated based on a real dataset from the Irish Smart Energy Trials. The results show a high performance of the proposed model compared to the models mentioned in the literature.