Szczegóły publikacji
Opis bibliograficzny
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 // Journal of Artificial Intelligence and Soft Computing Research [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2449-6499 . — 2023 — vol. 13 no. 3, s. 197–210. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 206–209, Abstr. — Publikacja dostępna online od: 2023-06-23. — J. Jaworek-Korjakowska - dod. afiliacja: Center of Excellence in Artificial Intelligence AGH
Autorzy (8)
- Niksa-Rynkiewicz Tacjana
- Stomma Piotr
- Witkowska Anna
- Rutkowska Danuta
- Słowik Adam
- Cpałka Krzysztof
- AGHJaworek-Korjakowska Joanna
- Kolendo Piotr
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150335 |
|---|---|
| Data dodania do BaDAP | 2024-01-04 |
| Tekst źródłowy | URL |
| DOI | 10.2478/jaiscr-2023-0015 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Artificial Intelligence and Soft Computing Research |
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.
