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基于误差分类的风电功率区间评估
作者:
作者单位:

中国矿业大学电气与动力工程学院,江苏省徐州市 221116

摘要:

为了提高风电功率的区间评估精度,结合预测误差数据的特性,提出了一种基于误差分类的区间评估方法。首先,引入K-means聚类算法,以欧氏距离为聚类指标对风电预测误差的整体水平进行分类。然后,建立误差区间评估模型,以风电功率数据和历史预测误差为模型输入,以预测误差区间为输出,利用长短期记忆(LSTM)神经网络深度学习模型输入和输出之间的关联。最后,利用Elia网站风电数据进行验证,结果表明,与其他评估模型和传统的误差概率分布方法相比,所提方法更能抓住误差数据的特性,能够得到更为准确的风电功率区间评估结果。

关键词:

基金项目:

国家自然科学基金资助项目(61703404)。

通信作者:

作者简介:

韩丽(1977—),女,通信作者,博士,教授,硕士生导师,主要研究方向:电力系统自动化、可再生能源发电技术以及电网优化调度等。E-mail:dannyli717@163.com
乔妍(1996—),女,硕士研究生,主要研究方向:可再生能源预测技术。E-mail:342805461@qq.com
景惠甜(1996—),女,硕士研究生,主要研究方向:可再生能源预测技术。E-mail:754704445@qq.com


Interval Estimation of Wind Power Based on Error Classification
Author:
Affiliation:

School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China

Abstract:

In order to improve the interval estimation accuracy of wind power, an interval estimation method based on error classification is proposed combined with the characteristics of prediction error data. Firstly, the K-means clustering algorithm is introduced to classify the overall levels of wind power prediction errors by using the Euclidean distance as the clustering index. Secondly, an error interval estimation model is established, of which the input is the wind power and the historical prediction error, and the output is the prediction error interval. Long short-term memory (LSTM) network is utilized to deeply learn the correlation between the input and output of the model. Finally, the wind power data from Elia website is used for verification. The results show that, compared with other estimation models and traditional error probability distribution methods, the proposed method can capture the characteristics of prediction error data and obtain more accurate interval estimation results of wind power.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 61703404).
引用本文
[1]韩丽,乔妍,景惠甜.基于误差分类的风电功率区间评估[J].电力系统自动化,2021,45(1):97-104. DOI:10.7500/AEPS20200227014.
HAN Li, QIAO Yan, JING Huitian. Interval Estimation of Wind Power Based on Error Classification[J]. Automation of Electric Power Systems, 2021, 45(1):97-104. DOI:10.7500/AEPS20200227014.
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  • 收稿日期:2020-02-27
  • 最后修改日期:2020-06-12
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  • 在线发布日期: 2021-01-05
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