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Abstract
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.
Citations
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Web of Science
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Authors (8)
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Piotr Stomma
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Danuta Rutkowska
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Adam Słowik
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Krzysztof Cpałka prof. dr hab. inż.
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Joanna Jaworek-Korjakowska
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Piotr Kolendo
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.2478/jaiscr-2023-0015
- License
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Journal of Artificial Intelligence and Soft Computing Research
no. 13,
pages 197 - 210,
ISSN: 2083-2567 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- 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:
- Digital Object Identifier (open in new tab) 10.2478/jaiscr-2023-0015
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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