Short-term solar irradiation forecast based on LSTM neural network

被引：0
|
作者：
Zhao S. [1 ]
Shang Y. [1 ]
Yang Y. [1 ]
Li Y. [1 ]
机构：
[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2021年 / 42卷 / 03期
关键词：
Deep learning; Long-short-term memory neural network; Recurrent neural network; Solar irradiation forecast;
D O I：
10.19912/j.0254-0096.tynxb.2017-0012
中图分类号：
学科分类号：
摘要：
This paper proposed a short-term solar irradiation forecast model based on long/short term memory neural network. The training samples with recursive structure are used to guarantee the time correlation within training sample. A prediction model based on long-short-term memory neural network is established and compared with the traditional prediction model based on artificial neural network. The results shown that compared with the traditional prediction model based on artificial neural network, the prediction model based on long-short-term memory neural network can significantly reduce the mean square error, which indicates that the model is more suitable for GHI prediction. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：383 / 388
页数：5
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