Big Data Platform for Smart Grids Power Consumption Anomaly
Detection

D 2019

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

LIPČÁK, Peter, Martin MACÁK and Bruno ROSSI

Basic information

Original name

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

Authors

LIPČÁK, Peter (703 Slovakia, belonging to the institution), Martin MACÁK (703 Slovakia, belonging to the institution) and Bruno ROSSI (380 Italy, guarantor, belonging to the institution)

Edition

New York, Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, p. 771-780, 10 pp. 2019

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14610/19:00110103

Organization unit

Institute of Computer Science

ISBN

978-1-5386-8005-6

DOI

http://dx.doi.org/10.15439/2019F210

UT WoS

000591782800108

Keywords in English

Computer architecture; Big Data; Smart meters; Real-time systems; Power demand; Energy management; Anomaly detection

Abstract

V originále

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).

Links

EF16_013/0001802, research and development project

Name: CERIT Scientific Cloud

LM2015085, research and development project

Name: CERIT Scientific Cloud (Acronym: CERIT-SC)

Investor: Ministry of Education, Youth and Sports of the CR, CERIT Scientific Cloud

Citovat

LIPČÁK, Peter, Martin MACÁK and Bruno ROSSI. Big Data Platform for Smart Grids Power Consumption Anomaly Detection. Online. In 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.

@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.

Detailed Information on Publication Record