Global Horizontal Irradiance Forecast at Kanto Region in Japan by Qunatile Regression of Support Vector Machine

被引：1
|
作者：
Takamatsu, Takahiro [1 ]
Ohtake, Hideaki [1 ,2 ]
Oozeki, Takashi [1 ]
机构：
[1] Natl Inst Adv Ind Sci & Technol, Koriyama, Fukushima 9630298, Japan
[2] Meterol Res Inst, Tsukuba, Ibaraki 3050052, Japan
来源：
2021 IEEE 48TH PHOTOVOLTAIC SPECIALISTS CONFERENCE (PVSC) | 2021年
关键词：
Quantile Regression; Support Vector Machine; Solar Power Forecast; Machine Learning;
D O I：
10.1109/PVSC43889.2021.9518856
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In the interests of the stable operation of the transmission system, transmission system operators (TSOs) procure regulating power supplies to cope with significant deviations from renewable energy forecasts. Therefore, it becomes important to improve the average precision of the one-day ahead forecast and to decrease the maximum error of the forecast in a power transmission system with a large number of photovoltaic systems. In this paper, the quantile regression using support vector machines is applied to the prediction of the previous day's solar radiation, and it is confirmed that maximum width of the error can be reduced while suppressing the minimum length of the prediction error.
引用
收藏
页码：2646 / 2647
页数：2
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