Electrical load forecasting on hierarchical levels of IPS of Ukraine using LSTM neural network

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Бібліографічні деталі
Дата:2021
Автори: S. S. Loskutov, P. V. Shymaniuk
Формат: Стаття
Мова:Англійська
Опубліковано: 2021
Назва видання:Proceedings of the Institute of Electrodynamics of the National Academy of Sciences of Ukraine
Онлайн доступ:http://jnas.nbuv.gov.ua/article/UJRN-0001274622
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Назва журналу:Library portal of National Academy of Sciences of Ukraine | LibNAS

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Library portal of National Academy of Sciences of Ukraine | LibNAS
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author S. S. Loskutov
P. V. Shymaniuk
author_facet S. S. Loskutov
P. V. Shymaniuk
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spelling open-sciencenbuvgovua-67332024-02-25T16:07:00Z Electrical load forecasting on hierarchical levels of IPS of Ukraine using LSTM neural network S. S. Loskutov P. V. Shymaniuk 1727-9895 2021 en Proceedings of the Institute of Electrodynamics of the National Academy of Sciences of Ukraine http://jnas.nbuv.gov.ua/article/UJRN-0001274622 Article
spellingShingle Proceedings of the Institute of Electrodynamics of the National Academy of Sciences of Ukraine
S. S. Loskutov
P. V. Shymaniuk
Electrical load forecasting on hierarchical levels of IPS of Ukraine using LSTM neural network
title Electrical load forecasting on hierarchical levels of IPS of Ukraine using LSTM neural network
title_full Electrical load forecasting on hierarchical levels of IPS of Ukraine using LSTM neural network
title_fullStr Electrical load forecasting on hierarchical levels of IPS of Ukraine using LSTM neural network
title_full_unstemmed Electrical load forecasting on hierarchical levels of IPS of Ukraine using LSTM neural network
title_short Electrical load forecasting on hierarchical levels of IPS of Ukraine using LSTM neural network
title_sort electrical load forecasting on hierarchical levels of ips of ukraine using lstm neural network
url http://jnas.nbuv.gov.ua/article/UJRN-0001274622
work_keys_str_mv AT ssloskutov electricalloadforecastingonhierarchicallevelsofipsofukraineusinglstmneuralnetwork
AT pvshymaniuk electricalloadforecastingonhierarchicallevelsofipsofukraineusinglstmneuralnetwork