Open AccessUltra Short Term Power Prediction of Offshore Wind Power Based on Support Vector Machine Optimized by Improved Dragonfly Algorithm
Author(s) -
Yan Yu,
Yingshuai Wu,
Ling Zhao,
Xiang Li,
Yanan Li
Publication year - 2021
Publication title -
distributed generation and alternative energy journal
Language(s) - English
Resource type - Journals
eISSN - 2156-3306
pISSN - 2156-6550
DOI - 10.13052/dgaej2156-3306.3734
Subject(s) - support vector machine , term (time) , power (physics) , computer science , wind power , relevance vector machine , offshore wind power , algorithm , data mining , engineering , artificial intelligence , physics , quantum mechanics , electrical engineering
In order to improve the prediction effect of ultra short term power of offshorewind power, the prediction model based on support vector machine optimizeddragony algorithm is constructed. Based on summary of the predictionmethods of wind power, the support vector machine optimized by dragonyalgorithm is established. Finally, prediction simulation analysis of offshorewind power is carried out, results show that the proposed prediction model inthis research can effectively improve the computing prediction precision.