Open AccessApplication of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid
Author(s) -
Nikolay Serebryakov
Publication year - 2021
Publication title -
omskij naučnyj vestnik
Language(s) - English
Resource type - Journals
eISSN - 2541-7541
pISSN - 1813-8225
DOI - 10.25206/1813-8225-2021-175-39-45
Subject(s) - electrical load , artificial neural network , term (time) , computer science , grid , artificial intelligence , convolutional neural network , adaptive learning , recurrent neural network , machine learning , engineering , electrical engineering , voltage , mathematics , physics , geometry , quantum mechanics
The article is devoted to the problem of improving the accuracy of short-term load forecasting of electrical engineering complex of regional electric grid with the use deep machine learning tools. The effectiveness of the application of the adaptive learning algorithm for deep neural networks for short-term load forecasting of this electrical complex has been investigated. The issues of application of convolutional and recurrent neural networks for short-term load forecasting are considered. A comparative analysis of the accuracy of the short-term load forecasting of electrical engineering complex of regional electric grid obtained using the ensemble neural network method and single neural networks are produced