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.
Highlights
Renewable energy, especially wind energy, has become the key to alleviating the energy problem.The installed capacity of wind power is increasing year by year and most wind farms are integrated into grids in the form of large-scale clusters
Autoregression (LASSO-VAR) which can take consideration of the historical data of all the wind farms in the region. 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
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
Summary
Especially wind energy, has become the key to alleviating the energy problem. 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. Compared to the previous wind power prediction method, it can take consideration of the global geographical location and make better use of the historical wind power and NWP information of wind farms in a region It can reduce normalized root mean square error (RMSE) in the fourth hour by 1.75%.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
