Regression Analysis of Meteorological Factors in Winter for Output Power of Photovoltaic Power Generation System

HOU Song-bao; WANG Kan-hong; SHI Kai-bo; KONG Li; ZHANG Ao-lin

doi:10.3969/j.issn.2095-560X.2017.05.012

Advances in New and Renewable Energy >

2017 , Vol. 5 >Issue 5: 403 - 408

DOI: https://doi.org/10.3969/j.issn.2095-560X.2017.05.012

Regression Analysis of Meteorological Factors in Winter for Output Power of Photovoltaic Power Generation System

HOU Song-bao ,
WANG Kan-hong ,
SHI Kai-bo ,
KONG Li ,
ZHANG Ao-lin

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1. Hebei University of Engineering, Handan 056038, China;
2. Nankai University, Tianjin 30071, China

Received date: 2017-09-01

Revised date: 2017-10-02

Online published: 2017-10-30

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Abstract

A prediction model for the output power of photovoltaic power generation system was built by using photovoltaic power output data and on-line monitoring of meteorological data from a photovoltaic power generation laboratory of a university in Hebei province. The direct correlation intensity between air quality and photovoltaic power was quantitatively analyzed by means of regression analysis. The direct influence and indirect interaction of daily average solar radiation intensity, average temperature, average wind speed, maximum air temperature and minimum air temperature were also considered. The results showed that the influence of solar radiation on the power generation of photovoltaic system was the most significant, while other factors have different effects. The influence of air quality on the photovoltaic system power generation should not be ignored. By using the regression model, the generation capacity of a small photovoltaic power station can be simply predicted based on the public meteorological data.

Key words： model regression; meteorological factors; photovoltaic; output power; air quality index

Cite this article

HOU Song-bao , WANG Kan-hong , SHI Kai-bo , KONG Li , ZHANG Ao-lin . Regression Analysis of Meteorological Factors in Winter for Output Power of Photovoltaic Power Generation System[J]. Advances in New and Renewable Energy, 2017 , 5(5) : 403 -408 . DOI: 10.3969/j.issn.2095-560X.2017.05.012

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