RESEARCH ON ULTRA-SHORT-TERM SOLAR IRRADIANCE PREDICTION METHOD AND DEVICEBASED ON GROUND-BASED CLOUD IMAGES

被引：0
|
作者：
Zhang Z. [1 ,2 ]
Chen T. [1 ]
Wang L. [1 ,2 ]
Shao X. [1 ]
Zhang Q. [1 ]
Ju X. [2 ]
机构：
[1] College of Mechanical and Electrical Engineering, Hohai University, Changzhou
[2] State Key Lab of New Energy and Energy Storage Operation Control, China Electric Power Research Institute Co.，Ltd., Beijing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 01期
关键词：
ground-based cloud image; image processing; irradiance prediction; machine learning; solar energy;
D O I：
10.19912/j.0254-0096.tynxb.2021-0912
中图分类号：
学科分类号：
摘要：
This paper proposes a cloud image exposure optimization method based on the all-sky imager，the ground-based cloud map is collected by the self-developed ground-based cloud map collection instrument，combined with cloud images with different exposures continuously shot at the same time，the cloud images are processed using dynamic range optimization algorithms. Perform feature extraction on the optimized cloud image，use image features as the input data of the prediction model，and establish a prediction model based on BP neural network. The verification results show that on the 5 min prediction scale，compared with the persistent model，the root mean square error of the established model is reduced by 14.31%. Compared with the existing research，the model established in this paper has a lower ERMSE. © 2023 Science Press. All rights reserved.
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页码：133 / 140
页数：7
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