###### Abstract
The advantage is that the accuracy of the model is higher than the two methods above when the structure of the model is well designed. The disadvantage is that the training time is relatively long and Graphics Processing Unit (GPU) is needed, which is suitable for offline training models and online application. The fourth kind of prediction method is the hybrid method. It often relates to the combination of different machine learning or deep learning methods and there are many varieties [11]. Some methods also decompose time series into several more predictable components by the empirical mode decomposition or variational mode decomposition, and then establish prediction method for each decomposed subsequence to increase the accuracy [12; 13]. It integrates the advantages of different methods and the structure can be adjusted according to the practical engineering scenario. Although the methods vary in forms, the performance of prediction methods is partly decided by the data used. From the perspective of input data, in addition to using single wind farm data directly, there are also some methods that use data from multiple wind farms. The relevant research and the actual observation of the wind farm in the field show that there is obvious correlation among wind farms [14; 15]. [PERSON] [16] brought out Least Absolute Shrinkage and Selection Operator-Vector Autoregression (LASSO-VAR) which can take consideration of the historical data of all the wind farms in the region. However, it is still a linear regression model. Deep learning such as classic convolutional neural network (CNN) [17] and stacked denoising auto-encoder (SDAE) [18] are introduce for the prediction of multiple wind farms. It can effectively model the time-varying and nonlinear effect among all the closely related wind farms, but it does not consider the global geographical relation of wind farms in the region when dealing with the complex spatial and temporal features. In most cases, we need to predict not only the power of each wind farm, but also the regional wind power. The additive method, extrapolation method and statistical scaling up method are commonly used [19]. The superposition method is to predict the power of all wind farms in the cluster, and simply sum the results. Extrapolation is a prediction method by comparing NWP with historical meteorological data to find similar scenarios through a historical database. The statistical scale-up method is to obtain the regional wind power output by multiplying the prediction results of the reference wind farm. In addition, there are some downscale methods for the spatial-temporal correlation analysis of wind power and wind speed [20; 21; 22]. The downscale method is about getting higher resolution wind speed or wind power from lower resolution NWP or prediction results. The downscaled NWP windspeed can provide more precise information for wind power prediction [23]. In fact, there are difficulties on two levels to build a comprehensive wind farm prediction model. The first one is how to use the complex spatial-temporal relationship effectively among the historical wind power data and NWP data of different wind farms, in order to increase the accuracy of prediction. The second is how to get the output of every single wind farm and the whole region efficiently, especially when the number of wind farms is big. Addressing these two goals, we proposed a hybrid prediction framework based on deep learning for wind power prediction in a region, calling it the Multi-modal Multi-task Graph Spatiotemporal NETwork (M2 GSNet). The main contribution is as follows:  
(1) We designed a spatiotemporal graph convolutional network which can extract the spatiotemporal feature of historical wind power and NWP data of wind farms in the given region. To the best of our knowledge, we are the first to employ a spectral graph neural network for the ultra-short-term wind farm cluster power prediction.