Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks

İnayet Özge Aksu; Tuğçe Demirdelen

doi:10.5152/tepes.2024.24018

Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks

Abstract

Electricity is one of the most important elements for economic growth and development of societies in today’s modern societies. The research of electricity generation, knowing the size of the electricity supply, and the methods developed to meet this supply are among the important subjects of study today. With the increase in electricity supply and the increasing importance of environmental pollution, the use of renewable energy sources in electricity generation is increasing. In this study, Long Short-Term Memory (LSTM), a type of recurrent neural network, is used to predict the energy production in a hydroelectric power plant. The LSTM method is one of the most popular recurrent neural network methods and is widely used in the field of deep learning. The graphical and numerical results obtained at the end of the study show the success and efficiency of the LSTM method. Ct represents the updated cell state. With ft, forgotten information is removed, with it, new information is added. In the last step, the output layer is obtained by using the equations given below.

Keywords

References

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Details

Primary Language

English

Subjects

Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics)

Journal Section

Research Article

Authors

İnayet Özge Aksu ^*
0000-0002-0963-2982
Türkiye

Tuğçe Demirdelen
0000-0002-1602-7262
Türkiye

Publication Date

October 31, 2024

Submission Date

July 8, 2024

Acceptance Date

August 7, 2024

Published in Issue

Year 2024 Volume: 4 Number: 3

DOI

https://doi.org/10.5152/tepes.2024.24018

IZ

https://izlik.org/JA62GH87PK

Cite

RIS / Bibtex

APA

Aksu, İ. Ö., & Demirdelen, T. (2024). Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks. Turkish Journal of Electrical Power and Energy Systems, 4(3), 159-164. https://doi.org/10.5152/tepes.2024.24018

AMA

1.Aksu İÖ, Demirdelen T. Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks. TEPES. 2024;4(3):159-164. doi:10.5152/tepes.2024.24018

Chicago

Aksu, İnayet Özge, and Tuğçe Demirdelen. 2024. “Forecasting Model of Electricity Production from Hydroelectric Sources With Long Short-Term Memory (LSTM) Networks”. Turkish Journal of Electrical Power and Energy Systems 4 (3): 159-64. https://doi.org/10.5152/tepes.2024.24018.

EndNote

Aksu İÖ, Demirdelen T (October 1, 2024) Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks. Turkish Journal of Electrical Power and Energy Systems 4 3 159–164.

IEEE

[1]İ. Ö. Aksu and T. Demirdelen, “Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks”, TEPES, vol. 4, no. 3, pp. 159–164, Oct. 2024, doi: 10.5152/tepes.2024.24018.

ISNAD

Aksu, İnayet Özge - Demirdelen, Tuğçe. “Forecasting Model of Electricity Production from Hydroelectric Sources With Long Short-Term Memory (LSTM) Networks”. Turkish Journal of Electrical Power and Energy Systems 4/3 (October 1, 2024): 159-164. https://doi.org/10.5152/tepes.2024.24018.

JAMA

1.Aksu İÖ, Demirdelen T. Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks. TEPES. 2024;4:159–164.

MLA

Aksu, İnayet Özge, and Tuğçe Demirdelen. “Forecasting Model of Electricity Production from Hydroelectric Sources With Long Short-Term Memory (LSTM) Networks”. Turkish Journal of Electrical Power and Energy Systems, vol. 4, no. 3, Oct. 2024, pp. 159-64, doi:10.5152/tepes.2024.24018.

Vancouver

1.İnayet Özge Aksu, Tuğçe Demirdelen. Forecasting Model of Electricity Production from Hydroelectric Sources with Long Short-Term Memory (LSTM) Networks. TEPES. 2024 Oct. 1;4(3):159-64. doi:10.5152/tepes.2024.24018