Prediction of Solar Radiation Intensity in Clear Sky Based on AOD Estimation Model

被引：0

作者：

Lin Yongjun ^{[1
]}

Xiong Feng ^{[1
]}

Liu Weiliang ^{[1
]}

Liu Changliang ^{[1
]}

Li Jing ^{[1
]}

Li Jintuo ^{[1
]}

机构：

[1] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Baoding 071003, Peoples R China

来源：

2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年

基金：

中央高校基本科研业务费专项资金资助;

关键词：

Solar Radiation Intensity; Aerosol Optical Depth; REST2; Model; PM2.5; AEROSOL OPTICAL-THICKNESS; IRRADIANCE; REST2;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Atmospheric aerosol is one of the most important factors that cause the random variation of solar radiation intensity. In view of the problem that the atmospheric aerosol optical depth (AOD) is difficult to obtain real-timely with high accuracy, BP neural network method is adopted to estimate the AOD based on PM2.5 concentration, PM10 concentration, air temperature and air relative humidity that from air quality monitoring station; then, take AOD and precipitable water as main change parameters, REST2 model is simplified to calculate direct solar radiation intensity and scattered radiation intensity in clear sky. Experimental results show that the proposed method for predicting solar radiation intensity in clear sky is of high precision and easy to be realized.

引用

页码：1452 / 1456

页数：5

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