Power forecasting of ultra-short-term photovoltaic station based on NWP similarity analysis

被引：0

作者：

Zhang S. ^{[1
]}

Dong L. ^{[1
]}

Ji D. ^{[1
]}

Hao Y. ^{[1
]}

Zhang X. ^{[2
]}

机构：

[1] Schoolof Automation, Beijing Institute of Technology, Beijing

[2] Zhuhai College of Beijing Institute of Technology, Zhuhai

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 04期

关键词：

Numerical weather prediction; Pearson correlation coefficient; Photovoltaic station; Power forecasting; Similarity analysis;

D O I：

10.19912/j.0254-0096.tynxb.2020-0717

中图分类号：

学科分类号：

摘要：

According to the fact that photovoltaic plants have similar generation power under similar weather conditions, an Ultra-short-term power forecasting method based on NWP similarity analysis is proposed. The proposed method uses the Pearson correlation coefficient to find weather forecast data similar to the predicted time, and estimates the power in the predicted time based on the actual power of the similar time. The proposed method can efficiently forecast the generated power based on the weather forecast data. Compared with the neural network, the proposed method has a better effect, especially in the period of large data fluctuations, which has higher reliability. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.

引用

页码：142 / 147

页数：5

共 16 条

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