Convolutional Neural Network for Short-term Solar Panel Output Prediction

被引：0
|
作者：
Sun, Yuchi [1 ]
Venugopal, Vignesh [1 ]
Brandt, Adam R. [1 ]
机构：
[1] Stanford Univ, Stanford, CA 94305 USA
来源：
2018 IEEE 7TH WORLD CONFERENCE ON PHOTOVOLTAIC ENERGY CONVERSION (WCPEC) (A JOINT CONFERENCE OF 45TH IEEE PVSC, 28TH PVSEC & 34TH EU PVSEC) | 2018年
关键词：
solar power prediction; convolutional neural networks; machine learning; photovoltaic cells; IRRADIANCE;
D O I：
暂无
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
The volatility of cloud movement introduced a large amount of uncertainty in short-term solar power prediction, which complicates modern power grid's operation. This work employs a specialized CNN model SUNSET, that utilizes both sky images and solar panel output history as input to predict 15-minute ahead solar panel generation. On a full year database, the model achieves 26.2% forecast skill on the sunny test set, and 16.1% forecast skill on the cloudy dataset. Both sky images and PV output history are shown to be pivotal model input, and two minutes is shown to be a suitable sampling frequency for this application.
引用
收藏
页码：2357 / 2361
页数：5
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