Big Data Platform for Smart Grids Power Consumption Anomaly Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F19%3A00110103" target="_blank" >RIV/00216224:14610/19:00110103 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8859779" target="_blank" >https://ieeexplore.ieee.org/document/8859779</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15439/2019F210" target="_blank" >10.15439/2019F210</a>
Alternative languages
Result language
angličtina
Original language name
Big Data Platform for Smart Grids Power Consumption Anomaly Detection
Original language 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).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2019 Federated Conference on Computer Science and Information Systems
ISBN
9781538680056
ISSN
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e-ISSN
2300-5963
Number of pages
10
Pages from-to
771-780
Publisher name
IEEE
Place of publication
New York
Event location
Leipzig, GERMANY
Event date
Jan 1, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000591782800108