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

doi:10.17775/CSEEJPES.2016.01920

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PV power forecasting using an integrated GA-PSO-ANFIS approach and Gaussian process regression based feature selection strategy

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- PV power forecasting using an integrated GA-PSO-ANFIS approach and Gaussian process regression based feature selection strategy
- PV power forecasting using an integrated GA-PSO-ANFIS approach and Gaussian process regression based feature selection strategy
- 中国电机工程学会电力与能源系统学报(英文) 2018年4卷第2期页码：210-218
- 作者机构：
  
  1. North China Electric Power University,, Beijing, CN,Beijing
  2. Goldwind Science and Technology Co., Ltd.,Beijing,China,100176
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2016.01920
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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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), 2018,4(2):210-218.

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]. CSEE Journal of Power and Energy Systems, 2018, 4(2): 210-218.
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), 2018,4(2):210-218. DOI： 10.17775/CSEEJPES.2016.01920.

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]. CSEE Journal of Power and Energy Systems, 2018, 4(2): 210-218. DOI： 10.17775/CSEEJPES.2016.01920.

摘要

Abstract

This paper presents a hybrid approach for the forecasting of electricity production in microgrids with solar photovoltaic (PV) installations. An accurate PV power generation forecasting tool essentially addresses the issues resulting from the intermittent and uncertain nature of solar power to ensure efficient and reliable system operation. A day-ahead

hourly mean PV power generation forecasting method based on a combination of genetic algorithm (GA)

particle swarm optimization (PSO) and adaptive neuro-fuzzy inference systems (ANFIS) is presented in this study. Binary GA with Gaussian process regression model based fitness function is used to determine important input parameters that significantly influence the amount of output power of a PV generation plant; and an integrated hybrid algorithm combining GA and PSO is used to optimize an ANFIS based PV power forecasting model for the plant. The proposed modeling technique is tested based on power generation data obtained from Goldwind microgrid system found in Beijing. Forecasting results demonstrate the superior performance of the proposed method as compared with commonly used forecasting approaches. The proposed approach outperformed existing artificial neural network (ANN)

linear regression (LR)

and persistence based forecasting models

validating its effectiveness.

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