基于时空注意力卷积模型的超短期风电功率预测  

Ultra-short-term Wind Power Prediction Based on Spatiotemporal Attention Convolution Model

作　　者：吕云龙 胡琴[1] 熊俊杰 龙敦华 LYU Yunong;HU Qin;XIONG Junjie;LONG Dunhua(Xuefeng Mountain Energy Equipment Safety National Observation and Research Station of Chongqing University,Shapingba District,Chongqing 400044,China;State Grid Jiangxi Electric Power Co.,Ltd.,Electric Power Research Institute,Nanchang 330096,Jiangxi Province,China)

机构地区：[1]重庆大学雪峰山能源装备安全国家野外科学观测研究站,重庆市沙坪坝区400044 [2]国网江西省电力有限公司电力科学研究院,江西省南昌市330096

出　　处：《电网技术》2024年第5期2064-2073,I0068,I0069-I0071,共14页Power System Technology

基　　金：国网江西省电力有限公司科技项目(521820220007)。

摘　　要：随着风电利用率的不断提高,风电输出功率的准确预测对电力系统的调度和稳定运行具有重要意义。然而,风力发电的随机性和波动性容易影响功率预测结果的准确性。该文提出一种基于时空相关性的风电功率预测方法,由时空注意力模块和时空卷积模块组成。首先,利用空间注意力层和时间注意力层对不同风机之间的时空相关性进行聚合提取。其次,通过空间卷积层和时间卷积层有效捕捉风电数据之间的空间特征和时间演变规律。最后,采用中国两处实际风电场运行数据对预测方法进行实验验证。结果表明,相比于传统预测方法,时空注意力和时空卷积的融合使该文所提出的预测方法具有较高的预测精度和较好的稳定性。With the continuous improvement of wind power utilization,accurate prediction of the wind power output power is of great significance for the scheduling and stable operating of the power systems.However,the randomness and volatility of the wind power generation easily affects the accuracy of the power prediction results.In this paper a wind power prediction based on the spatiotemporal correlation is proposed,consisting of a spatiotemporal attention module and a spatiotemporal convolution module.First,the spatial attention layer and the temporal attention layer are used to aggregate and extract the spatiotemporal correlations between different wind turbines.Second,the spatial features and the temporal evolution patterns among the wind power data are effectively captured by the spatial convolution layer and the temporal convolution layer.Finally,the prediction method is experimentally validated using the operational data from two actual wind farms in China.The results indicate that compared to the traditional prediction methods,the fusion of the spatiotemporal attention and the spatiotemporal convolution enables the proposed prediction to have a higher accuracy and a better stability.

关 键 词：风电功率预测 时空相关性 图神经网络 时空注意力模块 时空卷积模块 

分 类 号：TM721[电气工程—电力系统及自动化]