###### Abstract  
Ultra-short-term wind power prediction is of great importance for the integration of renewable energy. It is the foundation of probabilistic prediction and even a slight increase in the prediction accuracy can exert significant improvement for the safe and economic operation of power systems. However, due to the complex spatiotemporal relationship and the intrinsic characteristic of nonlinear, randomness and intermittence, the prediction of regional wind farm clusters and each wind farm's power is still a challenge. In this paper, a framework based on graph neural network and numerical weather prediction (NWP) is proposed for the ultra-short-term wind power prediction. First, the adjacent matrix of wind farms, which are regarded as the vertexes of a graph, is defined based on geographical distance. Second, two graph neural networks are designed to extract the spatiotemporal feature of historical wind power and NWP information separately. Then, these features are fused based on multi-modal learning. Third, to enhance the efficiency of prediction method, a multi-task learning method is adopted to extract the common feature of the regional wind farm cluster and it can output the prediction of each wind farm at the same time. The cases of a wind farm cluster located in Northeast China verified that the accuracy of a regional wind farm cluster power prediction is improved, and the time consumption increases slowly when the number of wind farms grows. The results indicate that this method has great potential to be used in large-scale wind farm clusters. graph neural network; multi-modal learning; multi-task learning; NWP; wind power prediction; wind farm cluster +
Footnote †: journal: Applied  
model is not easy to be obtained and it also leads to expansive computational costs. So, it is not suitable for the ultra-short-term wind power prediction directly. The statistic learning methods can be roughly divided into four categories. The first kind of prediction method is the classical multivariate time series prediction method, which has a relatively solid statistical theoretical foundation. In essence, it expands the traditional Autoregressive Integrated Moving Average model (ARIMA) on the multivariate time series and adopts the lasso method to select the key characteristic variables [2; 3]. The advantage is that the mathematical meaning is clear, and the model parameters are very convenient to adjust. Therefore, it is suitable for online application scenarios and is often used in practical fields. However, its disadvantage is that it is a linear model and does not consider the intrinsic relationships among variables. It often leads to larger error than other methods. The second kind of prediction method adopts the idea of the probabilistic graph model and models wind process as the Gaussian process [4; 5]. It uses the Gaussian function as the kernel function and can model the nonlinear data. It also can provide the confidence interval of the model and output the probabilistic distribution directly. However, the Gaussian process is a non-parametric model and every inference has to use each sample for the inverse calculation. It is intractable when the data volume is large. It is more precise than the first method, but it is still hard to capture the complex spatiotemporal relationship of wind process. The third kind of prediction method is the AI method. It includes both the traditional machine learning method and modern deep learning method. The machine learning method includes Back Propagation (BP) neural network [6], Decision Tree (DT) and its advanced derivatives Xgboost [7], and Extreme Learning Machine (ELM) [8]. The deep learning method is famous for its ability to extract the abstract features and different researchers use different models to predict the related energy time series. It includes Long Short-term Memory networks (LSTM) [9], Long- and Short-term Time-series network (LSTNet) [10] and so on.