Patent ID: 11874640
Assignee: WUHAN UNIVERSITY
Field: Electrical machinery, apparatus, energy (Electrical engineering)
Classification: CPC G  H | IPC G  H

Claim 5:
6. The system according to claim 5, wherein the predicting module is configured to, when using data of N time points to predict the wind power at the N+1 th time point, the multi-dimensional feature vector at the N time points needs to be used and mapped into the time sequence by the fully connected network in the encoder input layer, and then sequence information of the time sequence is encoded through the position encoding layer, after the sequence is encoded, the sequence enters the self-attention layer in the encoder layer so that the encoder can pay attention to the information of other coding sequences other than the current coding sequence, and the information of the sequence is retained through a neural network feedforward layer, the encoded time sequence passes through the plurality of encoder layers and is finally fed to the decoder, wherein the multi-dimensional feature vector at the N time points is a time sequence composed of related influence factors of wind power;
wherein the time sequence of wind power corresponding to the N time points is mapped into the time sequence through the decoder input layer, and the time sequence enters the encoding-decoding attention layer in the decoder layer to pay attention to correlation information of the sequence in the encoder and the decoder; after the feature vector and the time sequence of wind power are learned through the entire Transformer network, the output of the decoder is finally obtained through the fully connected layer containing one neuron, which is a predicted value of the wind power at the N+1th time point.