- Tytuł:
- An intelligent approach to short-term wind power prediction using deep neural networks
- Autorzy:
-
Niksa-Rynkiewicz, Tacjana
Stomma, Piotr
Witkowska, Anna
Rutkowska, Danuta
Słowik, Adam
Cpałka, Krzysztof
Jaworek-Korjakowska, Joanna
Kolendo, Piotr - Powiązania:
- https://bibliotekanauki.pl/articles/23944826.pdf
- Data publikacji:
- 2023
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
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 - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 197--210
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł