An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Niksa-Rynkiewicz, Tacjana; Stomma, Piotr; Witkowska, Anna; Rutkowska, Danuta; Słowik, Adam; Cpałka, Krzysztof; Jaworek-Korjakowska, Joanna; Kolendo, Piotr

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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Tacjana Niksa-Rynkiewicz

Niksa-Rynkiewicz, Tacjana

,

,

,

,

,

,

Joanna Jaworek-Korjakowska

Jaworek-Korjakowska, Joanna

et

Piotr Kolendo

Kolendo, Piotr

23 juin 2023

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks's Cover Image

Journal of Artificial Intelligence and Soft Computing Research

Édition 13 (2023): Edition 3 (Juin 2023)

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Publié en ligne: 23 juin 2023

Pages: 197 - 210

Reçu: 26 mai 2023

Accepté: 27 mai 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0015

Mots clés
Renewable Energy, Wind Energy, Wind Power, Wind Turbine, Short-Term Wind Power Prediction, Deep Learning, Convolutional Neural Networks, Gated Recurrent Unit, Hierarchical Multilayer Perceptron, Deep Neural Networks

© 2023 Tacjana Niksa-Rynkiewicz et al., published by Sciendo

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

Niksa-Rynkiewicz, Tacjana

Gdańsk University of Technology, Faculty of Ocean Engineering and Ship TechnologyGdańsk, Poland

Stomma, Piotr

University of Białystok, Institute of Computer ScienceBiałystok, Poland

Witkowska, Anna

Gdańsk University of Technology, Faculty of Electrical and Control EngineeringGdańsk, Poland

Rutkowska, Danuta

University of Social Sciences, Information Technology InstituteŁódź, Poland

Słowik, Adam

Koszalin University of Technology, Department of Electronics and Computer ScienceKoszalin, Poland

Cpałka, Krzysztof

Częstochowa University of Technology, Department of Intelligent Computer SystemsCzęstochowa, Poland

Jaworek-Korjakowska, Joanna

AGH University, Department of Automatic Control and Robotics, Center of Excellence in Artificial IntelligenceKraków, Poland

Kolendo, Piotr

Institute of Power Engineering, Department of Power AutomationGdańsk, Poland

Langue:: Anglais

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Sujets de la revue:: Informatique, Bases de données et exploration de données, Intelligence artificielle

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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Tacjana Niksa-Rynkiewicz

Piotr Stomma

Anna Witkowska

Danuta Rutkowska

Adam Słowik

Krzysztof Cpałka

Joanna Jaworek-Korjakowska

Piotr Kolendo

Publié en ligne: 23 juin 2023

Pages: 197 - 210

Reçu: 26 mai 2023

Accepté: 27 mai 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0015

Mots clésRenewable Energy, Wind Energy, Wind Power, Wind Turbine, Short-Term Wind Power Prediction, Deep Learning, Convolutional Neural Networks, Gated Recurrent Unit, Hierarchical Multilayer Perceptron, Deep Neural Networks

© 2023 Tacjana Niksa-Rynkiewicz et al., published by Sciendo

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

Mots clés
Renewable Energy, Wind Energy, Wind Power, Wind Turbine, Short-Term Wind Power Prediction, Deep Learning, Convolutional Neural Networks, Gated Recurrent Unit, Hierarchical Multilayer Perceptron, Deep Neural Networks