Short term wind speed prediction based on CEESMDAN and improved seagull optimization kernel extreme learning machine

Accurate wind speed predictions are crucial for the planning, operation, and energy management of wind farms. In this paper, we propose a novel wind speed prediction model, CEESMDAN-LNR-SOA-KELM. Firstly, we employ the CEESMDAN decomposition method to extract features from the original wind speed da...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Earth science informatics Ročník 18; číslo 1; s. 141
Hlavní autoři:	Qin, Xiwen, Yuan, Liping, Dong, Xiaogang, Zhang, Siqi, Shi, Hongyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025 Springer Nature B.V
Témata:	Algorithms Convergence Earth and Environmental Science Earth Sciences Earth System Sciences Energy management Information Systems Applications (incl.Internet) Machine learning Numerical experiments Ontology Optimization Parameters Prediction models Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Strategy Weather forecasting Wind farms Wind power Wind speed Seagull optimization algorithm Wind speed prediction Random opposition-based learning Adaptive decomposition Kernel extreme learning machine
ISSN:	1865-0473, 1865-0481
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!