EPJ Web Conf.
Volume 217, 2019International Workshop on Flexibility and Resiliency Problems of Electric Power Systems (FREPS 2019)
|Number of page(s)||6|
|Published online||15 October 2019|
The Use of Machine Learning in Situational Management in Relation to the Tasks of the Power Industry
Melentiev Energy Systems Institute of SB RAS, 664130 Irkutsk, st. Lermontov 130
2 Tomsk Polytechnic University, Department of Information Technologies, 634050, Tomsk, 30 Lenin Ave.
* Corresponding author: firstname.lastname@example.org
Published online: 15 October 2019
The article discusses the application possibilities of machine learning methods (artificial neural networks (ANN) and genetic algorithms (GA) to form management actions when applying the concept of situational management for intelligent support of strategic decision-making on the development of energy. At the first stage, the application of ANN to classify extreme situations in the energy sector, to select the most effective management actions (preventive measures) in order to prevent a critical situation from developing into an emergency. Genetic algorithms are proposed to be used to determine the weighting coefficients for training ANN. An algorithm for constructing a classifier based on a neural network and a demonstration task using data on generation and consumption of the United Electric Power System of Siberia are presented.
Key words: situational management / machine learning / artificial neural networks / genetic algorithms / extreme situations in the energy sector / management actions (preventive measures).
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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