Short-Term Wind Speed Forecasting Using Ensemble Learning

被引：4
|
作者：
Karthikeyan, M. [1 ]
Rengaraj, R. [2 ]
机构：
[1] Velammal Engn Coll, Dept Elect & Elect Engn, Chennai 600066, Tamil Nadu, India
[2] Sri Sivasubramaniya Nadar Coll Engn, Dept Elect & Elect Engn, Kalavakkam 603110, India
来源：
2021 7TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENERGY SYSTEMS (ICEES) | 2021年
关键词：
Short-term wind speed forecasting; ensemble learning; bagged trees; boosted trees; support vector regression;
D O I：
10.1109/ICEES51510.2021.9383718
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Wind speed forecasting plays a vital role in reliable operation and future planning of wind turbines in smart grid to meet growing power demand. This article presents ensemble learning based short-term wind speed forecasting. Regression models developed using boosted trees and bagged trees in ensemble learning are used to predict the wind speed. The regression models are trained and tested with historical dataset of Rameswaram located in India. Compared with support vector regression (SVR), ensemble-based wind speed forecasting performs short-term wind speed forecasting effectively.
引用
收藏
页码：502 / 506
页数：5
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