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...

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Veröffentlicht in:	Earth science informatics Jg. 18; H. 1; S. 141
Hauptverfasser:	Qin, Xiwen, Yuan, Liping, Dong, Xiaogang, Zhang, Siqi, Shi, Hongyu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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