Big Data Platform for Smart Grids Power Consumption Anomaly Detection

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Authors

LIPČÁK Peter MACÁK Martin ROSSI Bruno

Year of publication 2019
Type Article in Proceedings
Conference Proceedings of the 2019 Federated Conference on Computer Science and Information Systems
MU Faculty or unit

Institute of Computer Science

Citation
Web https://ieeexplore.ieee.org/document/8859779
Doi http://dx.doi.org/10.15439/2019F210
Keywords Computer architecture; Big Data; Smart meters; Real-time systems; Power demand; Energy management; Anomaly detection
Description 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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