| KRC, Rostislav, Martina KRATOCHVILOVA, Jan PODROUZEK, Tomas APELTAUER, Václav STUPKA and Tomáš PITNER. Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment. Sustainability. MDPI, 2021, vol. 13, No 5, p. 1-18. ISSN 2071-1050. Available from: https://dx.doi.org/10.3390/su13052954. |
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@article{2273658,
author = {Krc, Rostislav and Kratochvilova, Martina and Podrouzek, Jan and Apeltauer, Tomas and Stupka, Václav and Pitner, Tomáš},
article_number = {5},
doi = {http://dx.doi.org/10.3390/su13052954},
keywords = {smart grid; electricity network; flexibility assessment; renewable energy sources; machine learning; network simulation; artificial neural networks; convolutional neural networks},
language = {eng},
issn = {2071-1050},
journal = {Sustainability},
title = {Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment},
url = {https://doi.org/10.3390/su13052954},
volume = {13},
year = {2021}
}
TY - JOUR
ID - 2273658
AU - Krc, Rostislav - Kratochvilova, Martina - Podrouzek, Jan - Apeltauer, Tomas - Stupka, Václav - Pitner, Tomáš
PY - 2021
TI - Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment
JF - Sustainability
VL - 13
IS - 5
SP - 1-18
EP - 1-18
PB - MDPI
SN - 20711050
KW - smart grid
KW - electricity network
KW - flexibility assessment
KW - renewable energy sources
KW - machine learning
KW - network simulation
KW - artificial neural networks
KW - convolutional neural networks
UR - https://doi.org/10.3390/su13052954
N2 - 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.
ER -
KRC, Rostislav, Martina KRATOCHVILOVA, Jan PODROUZEK, Tomas APELTAUER, Václav STUPKA and Tomáš PITNER. Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment. \textit{Sustainability}. MDPI, 2021, vol.~13, No~5, p.~1-18. ISSN~2071-1050. Available from: https://dx.doi.org/10.3390/su13052954.
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