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KRČ, R. KRATOCHVÍLOVÁ, M. PODROUŽEK, J. APELTAUER, T. STUPKA, V. PITNER, T.
Original Title
Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment
Type
journal article in Web of Science
Language
English
Original Abstract
As energy distribution systems evolve from a traditional hierarchical load structure towards distributed smart grids, flexibility is increasingly investigated as both a key measure and core challenge of grid balancing. This paper contributes to the theoretical framework for quantifying network flexibility potential by introducing a machine learning based node characterization. In particular, artificial neural networks are considered for classification of historic demand data from several network substations. Performance of the resulting classifiers is evaluated with respect to clustering analysis and parameter space of the models considered, while the bootstrapping based statistical evaluation is reported in terms of mean confusion matrices. The resulting meta-models of individual nodes can be further utilized on a network level to mitigate the difficulties associated with identifying, implementing and actuating many small sources of energy flexibility, compared to the few large ones traditionally acknowledged.
Keywords
smart grid; electricity network; flexibility assessment; renewable energy sources; machine learning; network simulation; artificial neural networks; convolutional neural networks
Authors
KRČ, R.; KRATOCHVÍLOVÁ, M.; PODROUŽEK, J.; APELTAUER, T.; STUPKA, V.; PITNER, T.
Released
9. 3. 2021
Publisher
MDPI
Location
Basel, Switzerland
ISBN
2071-1050
Periodical
Sustainability
Year of study
13
Number
5
State
Swiss Confederation
Pages from
1
Pages to
18
Pages count
URL
https://www.mdpi.com/2071-1050/13/5/2954/pdf
Full text in the Digital Library
http://hdl.handle.net/11012/200892
BibTex
@article{BUT170530, author="Rostislav {Krč} and Martina {Floriánová} and Jan {Podroužek} and Tomáš {Apeltauer} and Václav {Stupka} and Tomáš {Pitner}", title="Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment", journal="Sustainability", year="2021", volume="13", number="5", pages="1--18", doi="10.3390/su13052954", issn="2071-1050", url="https://www.mdpi.com/2071-1050/13/5/2954/pdf" }