Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression

He Yaoyao; Li Haiyan; Wang Shuo; Yao Xin

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Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression

机译：基于立方样条插值的风电概率密度预测不确定性分析，支持向量分钟回归

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Accurate forecasting of wind power plays an important role in an effective and reliable power system. However, the fact of non-schedulability and fluctuation of wind power significantly increases the uncertainty of power systems. The output power of a wind farm is usually mixed with uncertainties, which reduce the effectiveness and accuracy of wind power forecasting. In order to handle the uncertainty of wind power, this paper first proposes to conduct outlier detection and reconstruct data before the prediction. Then, a wind power probability density forecasting method is proposed, based on cubic spline interpolation and support vector quantile regression (CSI-SVQR), which can better estimate the whole wind power probability density curve. However, the probability density prediction method can not acquire the optimal point prediction and interval prediction results at the same time. In order to analyze the uncertainty of wind power, the present study considers the prediction results from the perspective of probabilistic point prediction and interval prediction respectively. Three sets of real-world wind power data from Canada and China are used to validate the CSI-SVQR method. The results show that the proposed method not only efficiently eliminates the outliers of wind power but also provides the probability density function, offering a complete description of wind power generation fluctuation. Furthermore, more accurate point prediction and prediction interval (PI) can be obtained compared to existing methods. Wilcoxon signed rank test is used to verify that CSI can improve the performance of forecasting methods. (c) 2020 Elsevier B.V. All rights reserved.

机译：准确的风力预测在有效可靠的电力系统中起着重要作用。然而，不调度性和风能波动的事实显着提高了电力系统的不确定性。风电场的输出功率通常与不确定性混合，这降低了风力预测的有效性和准确性。为了处理风电的不确定性，本文首先提出在预测之前进行异常检测和重建数据。然后，基于立方样条插值和支持向量定量回归（CSI-SVQR），提出了一种风力概率密度预测方法，其能够更好地估计整个风力电力概率密度曲线。然而，概率密度预测方法不能同时获取最佳点预测和间隔预测结果。为了分析风力的不确定性，本研究分别考虑了概率点预测和间隔预测的视角。来自加拿大和中国的三套真实风电数据用于验证CSI-SVQR方法。结果表明，该方法不仅有效地消除了风力发电的异常值，还提供了概率密度函数，提供了对风力发电波动的完整描述。此外，与现有方法相比，可以获得更准确的点预测和预测间隔（PI）。 Wilcoxon签名等级测试用于验证CSI是否可以提高预测方法的性能。（c）2020 Elsevier B.v.保留所有权利。

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来源
《Neurocomputing》 |2021年第21期|121-137|共17页
作者
He Yaoyao; Li Haiyan; Wang Shuo; Yao Xin;
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作者单位

Hefei Univ Technol Sch Management Hefei 230009 Peoples R China|Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Peoples R China;

Hefei Univ Technol Sch Management Hefei 230009 Peoples R China|Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Peoples R China;

Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England;

Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England|Southern Univ Sci & Technol Shenzhen Key Lab Computat Intelligence Sch Comp Sci & Engn Shenzhen 518055 Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Wind power forecasting; Support vector quantile regression (SVQR); Cubic spline interpolation (CSI) function; Probability density forecasting;

机译：风电预测;支持向量分钟回归（SVQR）;立方样条插值（CSI）功能;概率密度预测;

Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression

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