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

Claim 3:
4. A wind power prediction system for optimizing a deep Transformer network, comprising:
a data acquisition module comprising processing circuitry configured to take a collected sequence data of wind power and related influencing factors as a sample data;
a data processing module comprising processing circuitry configured to perform maximum and minimum normalization processing on all of the sample data, and divide the normalized sample data into a training set and a test set;
an initialization module comprising processing circuitry configured to initialize Transformer network parameters, set a value range and a search range of hyper-parameters in the Transformer network to be optimized, and determine a maximum number of iteration and a population size of a whale group;
an optimal parameter acquisition module comprising processing circuitry configured to establish a Transformer network model according to the values of the hyper-parameters in the initialized Transformer network, respectively train and predict the data in the training set and the test set, and take an average absolute error of wind power prediction as a fitness value of each of the whale groups; determine a local optimal position according to the initial fitness value of the individual whale group, update a current optimal position by utilising whale group optimization, and obtain an optimal prediction effect by comparing a local optimal solution with a global optimal solution; obtain an optimal hyper-parameter combination in the Transformer network after a plurality of iterations of the whale optimization algorithm WOA;
a predicting module comprising processing circuitry configured to predict wind power by using the optimal parameters to construct a WOA-Transformer wind power prediction model,
wherein the optimal parameter acquisition module is configured to take the hyper-parameters in the Transformer network as individuals in the whale group, initialize the whale group, and use a random number generator to automatically generate an initial solution of the hyper-parameters in the Transformer network; if a value of a random parameter p is less than a first preset value, then it is determined whether a coefficient vector |A| is less than a second preset value, and if the coefficient vector |A| is less than the second preset value, then a shrink-envelopment predation mechanism is selected, a position of the individual is updated according to A=2a·r−a,C=2r and, a
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   ,, in the expression, Xj, Xj+1, and X*j respectively represent a current position, a position at the next moment, and an optimal position of the whale group, D represents a spatial distance between the current optimal position of the i-th whale and the optimal value, b is a logarithmic spiral constant, l is a random number and l∈[−1,1], calculate fitness values of all individuals in the whale group and update the global optimal value, and obtain the optimal hyper-parameter combination in the Transformer network after the plurality of iterations of the whale optimization algorithm WOA.