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

Claim 4:
5. The system according to claim 4, wherein the Transformer network model comprises an encoder and a decoder, wherein the encoder is composed of an input layer, a position encoding layer, and a plurality of identical encoder layers arranged in a stack, the input layer maps an input data into a multi-dimensional vector through a fully connected layer, the position encoding layer adds up an input vector and a position encoding vector element by element, and a vector obtained through addition is fed to each of the encoder layers, each of the encoder layers contains two sub-layers: a self-attention layer and a fully connected feedforward layer, each of the sub-layers is followed by a norm layer, and a multi-dimensional vector generated by the encoder is fed to the decoder;
wherein the decoder is composed of the input layer, multiple identical decoder layers and an output layer arranged in a stack, the input layer maps an input of decoder into the multi-dimensional vector, each of the decoder layers not only contains the two sub-layers in the encoder layer, but also is inserted with an encoding-decoding attention layer to apply a self-attention mechanism to an output of the encoder, the output layer contains a single neuron, the output layer maps the output of the last decoder layer to obtain a value for wind power prediction.