基于聚合时空图卷积网络的多风场超短期风速预测
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作者单位:

1.上海电力大学;2.润电能源科学技术有限公司

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基金项目:

国家自然科学基金青年计划(51607111)


Ultra-short-term Wind Speed Prediction Model for Multi Wind Farms Based on Aggregated Spatio-Temporal Graph Convolutional Networks
Author:
Affiliation:

1.College of Automation Engineering,Shanghai University of Electrical Power;2.Rundian Energy Science and Technology Co,Ltd

Fund Project:

National Natural Science Foundation of China,51607111.

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    摘要:

    在一定环境内区域风电场呈不规则分布的条件下,传统卷积神经网络(Convolutional Network, CNN)预测方法无法体现出各区域风场的分布状态和影响关系,难以实现对风速的准确预测。针对此问题,采用图卷积网络(Graph Convolutional Network, GCN)进行特征建模,并根据多风场的拓扑结构和各区域风场风速的互相关系数(Cross Correlation)建立连通图和权重矩阵。其次,依赖风场风速的时间动态特征,采用改进并列式卷积结构获取同一风场下多时间段的风速序列相关性。再次,利用风场风速的空间相关性和延时效应,采用二阶聚合方法将不同区域内风速的时空特征聚合。最后,经某区域风场数据验证表明,在0-4h预测尺度下该方法在多风场超短期风速预测中具有提取时空特征并提升预测性能的效果。

    Abstract:

    In a certain environment, the regional wind farms distribute irregularly. the traditional CNN prediction method can not reflect the distribution state and influence relationship of regional wind farms, and it is difficult to accurately predict wind speed. To solve this problem, GCN is used for feature modeling, and the connected graph and weight matrix are established according to the topology of multiple wind fields and the cross correlation coefficient of wind speed in each region. Secondly, Depending on the time dynamic characteristics of wind speed, the improved parallel convolution structure is used to obtain the correlation of wind speed series in multiple time periods under the same wind field. Thirdly, Using the spatial correlation and delay effect of wind speed, the spatio-temporal characteristics of wind speed in different regions are aggregated by second-order aggregation method. Finally, the verification of a regional wind field data shows that this method can extract spatio-temporal characteristics and improve the prediction performance in multi wind field ultra short-term wind speed prediction at the 0-4h prediction scale.

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  • 收稿日期:2021-12-27
  • 最后修改日期:2022-03-24
  • 录用日期:2022-03-25
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