ELECTRICAL LOAD FORECASTING ON HIERARCHICAL LEVELS OF IPS OF UKRAINE USING LSTM NEURAL NETWORK | Zendy

S.S. Loskutov | Zendy; Pavlo Shymaniuk | Zendy

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ELECTRICAL LOAD FORECASTING ON HIERARCHICAL LEVELS OF IPS OF UKRAINE USING LSTM NEURAL NETWORK

Author(s) -

S.S. Loskutov,

Pavlo Shymaniuk

Publication year - 2021

Publication title -

praci institutu elektrodinamiki nacionalanoi akademii nauk ukraini

Language(s) - English

Resource type - Journals

eISSN - 1727-9895

pISSN - 2786-7064

DOI - 10.15407/publishing2021.59.081

Subject(s) - artificial neural network , stability (learning theory) , computer science , electric power system , artificial intelligence , table (database) , power quality , electrical load , power (physics) , electrical network , electric power , machine learning , data mining , engineering , electrical engineering , physics , quantum mechanics

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.

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