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

Abstrakt

In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

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Wersja publikacji
Accepted albo Published Version
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.2478/jaiscr-2023-0015
Licencja
Creative Commons: CC-BY-NC-ND otwiera się w nowej karcie

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Journal of Artificial Intelligence and Soft Computing Research nr 13, strony 197 - 210,
ISSN: 2083-2567
Język:
angielski
Rok wydania:
2023
Opis bibliograficzny:
Niksa-Rynkiewicz T., Stomma P., Witkowska A., Rutkowska D., Słowik A., Cpałka K., Jaworek-Korjakowska J., Kolendo P.: An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks// Journal of Artificial Intelligence and Soft Computing Research -Vol. 13,iss. 3 (2023), s.197-210
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.2478/jaiscr-2023-0015
Źródła finansowania:
  • Publikacja bezkosztowa
Weryfikacja:
Politechnika Gdańska

wyświetlono 111 razy

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