Recurrent Neural Network-Based Autoencoder for Problems of Automatic Time Series Analysis at Power Facilities
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Recurrent Neural Network-Based Autoencoder for Problems of Automatic Time Series Analysis at Power Facilities
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| Автор |
Matrenin, P. V.
Khalyasmaa, A. I. Potachits, Y. V. |
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| Тематика |
AUTOENCODER
OPERATING PARAMETERS OF ELECTRICAL NETWORKS RECURRENT NEURAL NETWORKS TIME SERIES PROCESSING |
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| Описание |
Digitalization of the energy sector leads to an increase in the volume and rate of data collection. A primary barrier to the proper management of the technological data is the lack of data labeling corresponding to emergency modes, power equipment technical state, etc. Thus, despite the large amount of data, there is a shortage of labeled data suitable for training, validating and testing the machine learning models. Labeling by an expert takes too much time, so there is an actual task to automatically identify data fragments that are potentially of interest. The aim of the work is to develop an algorithm for prioritizing the fragments of the time series using the compact recurrent autoencoder. To achieve the goal, a neural network architecture was developed based on recurrent encoding and decoding cells, capable of unsupervised learning. The model was tested on two data sets: a synthetic sinusoidal signal with missing values and electric current measurements with thermal limit deviations. The substantial results of the work are the compact architecture of the autocoding model and the high interpretability of the output. The most significant achievements of the study are both the autocoding neural network model, which does not require initial assumption about the type of deviations, and the proposed algorithm for prioritizing the data fragments. The significance of the results is prooved by the reduction of the time for analyzing and labeling large data arrays with technological parameters of the electrical networks, which allows using these data for training, validating and testing. © 2023 Sovero Press Publishing House. All rights reserved.
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| Дата |
2024-04-05T16:28:27Z
2024-04-05T16:28:27Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Matrenin, PV, Khalyasmaa, AI & Potachits, YV 2023, 'Автокодирующая рекуррентная нейронная сеть для задач автоматизации анализа временных рядов на объектах энергетики', Problems of the Regional Energetics, № 2(58), стр. 61-71. https://doi.org/10.52254/1857-0070.2023.2-58-06
Matrenin, P. V., Khalyasmaa, A. I., & Potachits, Y. V. (2023). Автокодирующая рекуррентная нейронная сеть для задач автоматизации анализа временных рядов на объектах энергетики. Problems of the Regional Energetics, (2(58)), 61-71. https://doi.org/10.52254/1857-0070.2023.2-58-06 1857-0070 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165252958&doi=10.52254%2f1857-0070.2023.2-58-06&partnerID=40&md5=294f049f9a4923f9def956a12aa97a12 https://doi.org/10.52254/1857-0070.2023.2-58-06 http://elar.urfu.ru/handle/10995/130651 10.52254/1857-0070.2023.2-58-06 85165252958 000994818300006 |
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| Язык |
ru
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Institute of Power Engineering
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| Источник |
Problems of the Regional Energetics
Problems of the Regional Energetics |
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