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PV Power Forecasting Using an Integrated GA-PSO-ANFIS Approach and Gaussian Process Regression Based Feature Selection Strategy

Yordanos Kassa Semero Jianhua Zhang Dehua Zheng

中国电机工程学会电力与能源系统学报（英文版）2018，Vol.4Issue(2)：210-218,9.

中国电机工程学会电力与能源系统学报（英文版）2018，Vol.4Issue(2)：210-218,9.DOI:10.17775/CSEEJPES.2016.01920

PV Power Forecasting Using an Integrated GA-PSO-ANFIS Approach and Gaussian Process Regression Based Feature Selection Strategy

Yordanos Kassa Semero ¹Jianhua Zhang ¹Dehua Zheng²

作者信息

1. School of Electrical and Electronic Engineering,North China Electric Power University, Beijing 102206, China
2. Goldwind Science and Technology Co., Ltd.,Beijing 100176, China
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摘要

关键词

ANFIS/binary genetic algorithm/feature selection/hybrid method/particle swarm optimization/PV power forecasting

Key words

ANFIS/binary genetic algorithm/feature selection/hybrid method/particle swarm optimization/PV power forecasting

引用本文复制引用

Yordanos Kassa Semero,Jianhua Zhang,Dehua Zheng..PV Power Forecasting Using an Integrated GA-PSO-ANFIS Approach and Gaussian Process Regression Based Feature Selection Strategy[J].中国电机工程学会电力与能源系统学报（英文版）,2018,4(2):210-218,9.

中国电机工程学会电力与能源系统学报（英文版）

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ISSN：2096-0042

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