Szczegóły publikacji
Opis bibliograficzny
Wind power forecasts and network learning process optimization through input data set selection / Mateusz DUTKA, Bogusław ŚWIĄTEK, Zbigniew HANZELKA // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073. — 2023 — vol. 16 iss. 6 art. no. 2562, s. 1-36. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 34-36, Abstr. — Publikacja dostępna online od: 2023-03-08
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 145811 |
|---|---|
| Data dodania do BaDAP | 2023-03-31 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en16062562 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
Energy policies of the European Union, the United States, China, and many other countries are focused on the growth in the number of and output from renewable energy sources (RES). That is because RES has become increasingly more competitive when compared to conventional sources, such as coal, nuclear energy, oil, or gas. In addition, there is still a lot of untapped wind energy potential in Europe and worldwide. That is bound to result in continuous growth in the share of sources that demonstrate significant production variability in the overall energy mix, as they depend on the weather. To ensure efficient energy management, both its production and grid flow, it is necessary to employ forecasting models for renewable energy source-based power plants. That will allow us to estimate the production volume well in advance and take the necessary remedial actions. The article discusses in detail the development of forecasting models for RES, dedicated, among others, to wind power plants. Describes also the forecasting accuracy improvement process through the selection of the network structure and input data set, as well as presents the impact of weather factors and how much they affect the energy generated by the wind power plant. As a result of the research, the best structures of neural networks and data for individual objects were selected. Their diversity is due to the differences between the power plants in terms of location, installed capacity, energy conversion technology, land orography, the distance between turbines, and the available data set. The method proposed in the article, using data from several points and from different meteorological forecast providers, allowed us to reduce the forecast error of the NMAPE generation to 3.3%.