Wind power short-term power prediction method based on deep learning and multiple integration

The invention discloses a wind power short-term power prediction method based on deep learning and multiple integration. The wind power short-term power prediction method comprises the following steps: step 1, acquiring meteorological data of an area where wind power to be predicted is located by using a data collection device; step 2, the data collection device preprocesses the acquired meteorological data through a preprocessing module; 3, aiming at different variables of the meteorological data, carrying out normalization processing by adopting a standard method; step 3, analyzing correlation among different variables of the meteorological data; 4, designing a deep and multiple integrated neural network structure; 5, training the deep and multiple integrated neural network by using the training sample data; and 6, in a wind power short-term power prediction module, predicting the wind power short-term power of the test sample by using the trained depth and multiple integrated neural network model. Accordin