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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. In \textit{Proceedings of the 2019 Federated Conference on Computer Science and Information Systems}. New York: IEEE. p.~771-780. ISBN~978-1-5386-8005-6. doi:10.15439/2019F210. 2019.
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