Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method

IF 3.1 4区工程技术 Q3 ENERGY & FUELS

International Journal of Green Energy Pub Date : 2024-03-27 DOI:10.1080/15435075.2024.2334498

Yuwei Liu, Lingling Li, Jiaqi Liu

引用次数: 0

Abstract

Affected by the uncertainty of external environmental factors, wind power generation has significant characteristics of randomness and non-stationarity. Accurately predicting wind power is a necess...

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使用混合增强海鸥优化算法和支持向量机预测短期风力发电量：一种高精度方法

受外部环境不确定性因素的影响，风力发电具有明显的随机性和非稳定性特征。准确预测风力发电量是一项必要...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Green Energy 工程技术-能源与燃料

CiteScore

6.60

自引率

9.10%

发文量

112

审稿时长

3.7 months

期刊介绍： International Journal of Green Energy shares multidisciplinary research results in the fields of energy research, energy conversion, energy management, and energy conservation, with a particular interest in advanced, environmentally friendly energy technologies. We publish research that focuses on the forms and utilizations of energy that have no, minimal, or reduced impact on environment, economy and society.