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Article

Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment

1
Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, 602 00 Brno, Czech Republic
2
Centre for Education, Research and Innovation in Information and Communication Technologies-ExecUnit, Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
Academic Editors: Mehdi Seyedmahmoudian, Alex Stojcevski, Ben Horan, Saad Mekhilef and Eklas Hossain
Sustainability 2021, 13(5), 2954; https://doi.org/10.3390/su13052954
Received: 26 January 2021 / Revised: 20 February 2021 / Accepted: 1 March 2021 / Published: 9 March 2021
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. View Full-Text
Keywords: smart grid; electricity network; flexibility assessment; renewable energy sources; machine learning; network simulation; artificial neural networks; convolutional neural networks smart grid; electricity network; flexibility assessment; renewable energy sources; machine learning; network simulation; artificial neural networks; convolutional neural networks
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MDPI and ACS Style

Krč, R.; Kratochvílová, M.; Podroužek, J.; Apeltauer, T.; Stupka, V.; Pitner, T. Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment. Sustainability 2021, 13, 2954. https://doi.org/10.3390/su13052954

AMA Style

Krč R, Kratochvílová M, Podroužek J, Apeltauer T, Stupka V, Pitner T. Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment. Sustainability. 2021; 13(5):2954. https://doi.org/10.3390/su13052954

Chicago/Turabian Style

Krč, Rostislav, Martina Kratochvílová, Jan Podroužek, Tomáš Apeltauer, Václav Stupka, and Tomáš Pitner. 2021. "Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment" Sustainability 13, no. 5: 2954. https://doi.org/10.3390/su13052954

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