Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak gen...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access Jg. 9; S. 94040 - 94052
Hauptverfasser:	An, Guoqing, Jiang, Ziyao, Cao, Xin, Liang, Yufei, Zhao, Yuyang, Li, Zheng, Dong, Weichao, Sun, Hexu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaboost algorithm Algorithms Artificial neural networks Electric power grids Electricity distribution extreme learning machine Machine learning Model accuracy Neural networks Optimization optimization algorithm Particle swarm optimization Prediction algorithms Prediction models Predictive models Support vector machines Thresholds Training Wind forecasting Wind power Wind power generation wind power prediction Wind speed Wind turbines
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!