Short-term forecasting method of wind power generation based on BP neural network with combined loss function

被引：5

作者：

Liu F. ^{[1
]}

Wang Z. ^{[1
]}

Liu R.-D. ^{[1
]}

Wang K. ^{[2
]}

机构：

[1] College of Electrical Engineering, Zhejiang University, Hangzhou

[2] Department of Computer Science, University of Chinese Academy of Science, Beijing

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2021年 / 55卷 / 03期

关键词：

Artificial neural network; Feature extraction; Loss function; Power segment; Wind power forecast;

D O I：

10.3785/j.issn.1008-973X.2021.03.021

中图分类号：

学科分类号：

摘要：

A short-term forecasting model of neural network for wind power generation with the combined loss function was proposed, in order to reduce the side effect of large-scale wind power integration on power system energy balance and increase system's wind power accommodation ability. A classification method was introduced into the model, and a BP neural network short-term wind power prediction model with the goal of minimizing the combined loss function was proposed, in order to improve the utilization of raw data information. The combined loss function was constructed by the mean square error loss function, the cross-entropy loss function and the rank loss function according to different weight ratios. Compare to the prediction method based on separate loss functions, the combined loss function proposed can effectively improve the prediction accuracy from real wind farm data test. Copyright ©2021 Journal of Zhejiang University (Engineering Science). All rights reserved.

引用

页码：594 / 600

页数：6

共 21 条

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