ELECTRICAL LOAD FORECASTING ON HIERARCHICAL LEVELS OF IPS OF UKRAINE USING LSTM NEURAL NETWORK
Article_11 PDF (Українська)

Keywords

forecasting
total electric load
neural networks
recurrent neural networks

How to Cite

Loskutov, S.S., and P.V. Shymaniuk. “ELECTRICAL LOAD FORECASTING ON HIERARCHICAL LEVELS OF IPS OF UKRAINE USING LSTM NEURAL NETWORK”. Proceedings of the Institute of Electrodynamics of the National Academy of Sciences of Ukraine, no. 59, Sept. 2021, p. 081, doi:10.15407/publishing2021.59.081.

Abstract

The scientific research presents the results of a study of one-factor forecasting of the total electrical load at three hierarchical levels of the integrated power system (IPS) of Ukraine using artificial neural networks, such as LSTM. Based on research, forecasting errors at each hierarchical level of the power system were analyzed. Methods for improving the quality and stability of forecasts were proposed. The obtained results are the basis for the study of the assessment of the accuracy of forecasting the summary electrical load in the IPS of Ukraine. Ref. 9, fig. 4, table.

https://doi.org/10.15407/publishing2021.59.081
Article_11 PDF (Українська)

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2021 S.S. Loskutov, P.V. Shymaniuk

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