Wind power output prediction in complex terrain based on modal decomposition attentional convolutional network

Abstract

In this work, modal decomposition is employed to generate more data for matching scenarios with more complex topography for predicting wind power output in the case of complex terrain. The existing literature shows that a single wind power output forecast model is difficult to cope with complex terrain and thus the accuracy of wind power output forecast is limited. This work combines the advantages of attention mechanism and convolutional neural network for a novel network based on modal decomposition of historical data for wind power output forecast on complex terrain. The proposed novel network can break through the limitations of a single wind power output forecast model. In addition, the signals that are modally decomposed can be predicted more accurately. The presented method is contrasted with various other algorithms for the wind power output prediction problem in complex terrain. Comparative experiments show that the proposed network achieves a higher accuracy rate.