Smart Grids offer multiple benefits: efficient energy provision, quicker recoveries from failures, etc. Nevertheless, there is risk of data tampering, unsolicited modification of the data of the smart meters. The main aim of this paper is to provide a model for processing the smart meter data that flags any energy consumption level that could be indication of data tampering. The proposed model is time-sensitive, allowing for tracking the energy usage along time, thus making possible the detection of long-lasting abnormal levels of energy consumption. Such model can be integrated in an anomaly detection system and in a semantic web reasoner.
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Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).
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