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Monitoring energy consumption and diagnosing abnormal behavior will enable utilities to introduce strategies to improve system resiliency, stability, and to meet energy efficiency targets. The deployment of advanced metering infrastructure (AMI) enables utilities to collect various raw data from its customers and networks. This paper presents contextual anomaly detection algorithm to detect irregular power consumption and visualize anomaly scores using unsupervised learning algorithm and temporal context generated from meter readings. The proposed algorithm computes an anomaly score for each user by considering historical consumption data. The anomaly score for a user is then adjusted by analyzing other contextual variables such as seasonal variation day of the week and other users with the same historical pattern. The implementation of realworld data set provided by power utility company shows a high performance of the proposed algorithm.

Research in frequent pattern mining from streaming data becomes a pioneer in the field of information systems. The data stream is a continuous flow of data generated from different sources. Extracting frequent patterns from streaming data raises new challenges for the data mining community. We present an overview of the growing field of data streams. Many applications handle streaming data such as sensor networks, traffic management, log data, telephone call records, and social networks. These applications generate high volumes of streaming data with velocity, which is difficult to handle with traditional data mining techniques. This paper mainly reviewed different research algorithms, scientific practices, and methods that have been developed for mining frequent patterns from streaming data. In addition, it discusses well-known open-source software and tools for data stream mining, which are developing to handle streaming data. Finally, it summarizes the open issues and challenges to current existing approaches while handling and processing data streams in realworld applications.