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@inproceedings{1544717, author = {Lipčák, Peter and Macák, Martin and Rossi, Bruno}, address = {New York}, booktitle = {Proceedings of the 2019 Federated Conference on Computer Science and Information Systems}, doi = {http://dx.doi.org/10.15439/2019F210}, keywords = {Computer architecture; Big Data; Smart meters; Real-time systems; Power demand; Energy management; Anomaly detection}, howpublished = {elektronická verze "online"}, language = {eng}, location = {New York}, isbn = {978-1-5386-8005-6}, pages = {771-780}, publisher = {IEEE}, title = {Big Data Platform for Smart Grids Power Consumption Anomaly Detection}, url = {https://ieeexplore.ieee.org/document/8859779}, year = {2019} }
TY - JOUR ID - 1544717 AU - Lipčák, Peter - Macák, Martin - Rossi, Bruno PY - 2019 TI - Big Data Platform for Smart Grids Power Consumption Anomaly Detection PB - IEEE CY - New York SN - 9781538680056 KW - Computer architecture KW - Big Data KW - Smart meters KW - Real-time systems KW - Power demand KW - Energy management KW - Anomaly detection UR - https://ieeexplore.ieee.org/document/8859779 L2 - https://ieeexplore.ieee.org/document/8859779 N2 - 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). ER -
LIPČÁK, Peter, Martin MACÁK and Bruno ROSSI. Big Data Platform for Smart Grids Power Consumption Anomaly Detection. Online. In \textit{Proceedings of the 2019 Federated Conference on Computer Science and Information Systems}. New York: IEEE, 2019, p.~771-780. ISBN~978-1-5386-8005-6. Available from: https://dx.doi.org/10.15439/2019F210.
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