Result Details
Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment
Floriánová Martina, Ing., Ph.D., AIU (FCE)
Podroužek Jan, doc. Dr.techn. Ing., AIU (FCE), STM (FCE)
Apeltauer Tomáš, doc. Mgr., Ph.D., AIU (FCE), AIU-AdMaS (FCE), PKO (FCE)
Stupka Václav, Mgr., Ph.D., UTKO (FEEC)
Pitner Tomáš, doc. RNDr., Ph.D.
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.
smart grid; electricity network; flexibility assessment; renewable energy sources; machine
learning; network simulation; artificial neural networks; convolutional neural networks
@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",
url="https://www.mdpi.com/2071-1050/13/5/2954/pdf"
}