A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression

被引:112
作者
Naik, Jyotirmayee [1 ]
Dash, Pradipta Kishore [1 ]
Dhar, Snehamoy [1 ]
机构
[1] Siksha O Anusandhan Deemed Be Univ, Bhubaneswar, Odisha, India
关键词
Wind power and wind speed prediction; Prediction intervals; Variational mode decomposition; Multi-kernel robust ridge regression; Multi-objective chaotic water cycle algorithm; WATER CYCLE ALGORITHM; GENETIC ALGORITHM; MACHINE; CHAOS; LOAD; MAP;
D O I
10.1016/j.renene.2019.01.006
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents a new hybrid multi-objective wind speed and wind power prediction interval forecasting (PIs) model which is the combination of variational mode decomposition (VMD), Multi-kernel robust ridge regression (MKRR) and a multi-objective Chaotic water cycle algorithm (MOCWCA). VMD is applied to decompose the main time series signals into appropriate number of modes that avoids the mutual effects present in between the modes. The VMD based MKRR method is applied to estimate the wind speed and wind power prediction intervals at a prediction interval nominal confidence levels (PINC) of 95%, 90%,85% and 80%, respectively. Further to improve the performance of the proposed prediction model MOCWCA is introduced for the optimization of the prediction models parameters in such a way that multiple objectives are satisfied to produce Pareto-optimal solutions. The wind speed and power data samples for prediction interval forecasting are collected at 30 min and 1 hour time intervals from the Sotavento wind farm located in Spain. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:701 / 731
页数:31
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