In reality, wind power data are often accompanied by data losses, which can affect the accurate prediction of wind power and subsequently impact the real-time scheduling of the power system. Existing methods for recovering missing data primarily consider the environmental conditions of individual wind farms, thereby overlooking the spatiotemporal correlations between neighboring wind farms, which significantly compromise their recovery effectiveness. In this paper, a joint missing data recovery model based on power data from adjacent wind farms is proposed. At first, a spatial–temporal module (STM) is designed using a combination of graph convolution network and recurrent neural networks to learn spatiotemporal dependencies and similarities. Subsequently, to provide a solid computational foundation for the STM, a Euclidean-directed graph based on Granger causality is constructed to reflect the hidden spatiotemporal information in the data. Finally, comprehensive tests on data recovery for both missing completely at random and short-term continuous missing are conducted on a real-world dataset. The results demonstrate that the proposed model exhibits a significant advantage in missing data recovery compared to baseline models.

中文翻译：

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基于多风电场时空相关性的联合缺失功率数据恢复方法

现实中，风电数据往往伴随着数据丢失，影响风电功率的准确预测，进而影响电力系统的实时调度。现有的缺失数据恢复方法主要考虑单个风电场的环境条件，从而忽略了相邻风电场之间的时空相关性，这极大地影响了其恢复效果。本文提出了一种基于相邻风电场电力数据的联合缺失数据恢复模型。首先，使用图卷积网络和循环神经网络的组合设计时空模块（STM）来学习时空依赖性和相似性。随后，为了给STM提供坚实的计算基础，构建了基于格兰杰因果关系的欧几里得有向图来反映数据中隐藏的时空信息。最后，在真实数据集上对完全随机丢失和短期连续丢失的数据恢复进行了全面的测试。结果表明，与基线模型相比，所提出的模型在丢失数据恢复方面表现出显着的优势。