Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine

Precise prediction of wind power can not only conduct wind turbine's operation, but also reduce the impact on power systems when wind energy is injected into the grid. A hybrid autoregressive fractionally integrated moving average and least square support vector machine model is proposed to for...

Full description

Saved in:
Bibliographic Details
Published in:Energy (Oxford) 2017-06, Vol.129, p.122-137
Main Authors: Yuan, Xiaohui, Tan, Qingxiong, Lei, Xiaohui, Yuan, Yanbin, Wu, Xiaotao
Format: Article
Language:English
Subjects:
autocorrelation
Autocorrelation function
Autoregressive fractionally integrated moving average
Data processing
Electricity distribution
Function analysis
Least square support vector machine
Least squares method
Linear equations
Long memory characteristics
Mathematical models
Power series
prediction
Regression analysis
Studies
support vector machines
Turbines
Wind direction
Wind power
Wind power generation
Wind power prediction
Wind speed
wind turbines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!