Wind power prediction method based on AFSA-GNN

Abstract

The embodiment of the invention provides a wind power prediction method based on AFSA-GNN, and the method comprises the steps: carrying out the cleaning of obtained wind power data, and carrying out the normalization processing; building an RNN model, and determining the number of hidden layer nodes of the RNN model; performing regression operation based on the obtained RNN model to obtain predicted power, and constructing an objective function by combining the minimum root-mean-square error of the predicted power and the actually measured power; initializing various parameters of an AFSA algorithm, and optimizing the weight of the RNN model by using the steps of the AFSA algorithm; and inputting the processed wind power data into the optimized RNN model for wind power prediction to obtain a wind power output power prediction value. The optimization of the connection weight of the ridgelet neural network is quickly realized by using the excellent optimization ability of the artificial fish swarm algorithm and taking the minimum RMSE as a target function, and the wind power output power prediction model is established by using the quick approximation ability of the ridgelet neural network to high-dimensional data, so that the prediction precision can be effectively improved.

Description

technical field

[0001] This application relates to the field of wind power, and in particular to an AFSA-GNN-based wind power prediction method. Background technique

[0002] There is a broad consensus on the development and utilization of renewable energy and the promotion of low-carbon green transformation of the economy. As a clean renewable energy, wind energy has been more and more widely used. However, due to the intermittence, volatility and randomness of wind energy itself, the output power of wind power fluctuates greatly and changes quickly. After wind power is connected to the grid, it will bring great challenges to the stable operation of the power system. Therefore, accurate prediction of wind power output can effectively improve the impact on the power system, and provide a basis for power dispatching departments to rationally arrange conventional energy generation and wind power generation, and to adjust power generation plans in a timely manner.

[0003] At ...

Examples

Embodiment Construction

[0039] In order to make the structure and advantages of the present application clearer, the structure of the present application will be further described below in conjunction with the accompanying drawings.

[0040] Specifically, the wind power prediction method based on AFSA-GNN proposed in the embodiment of this application, such as figure 1 shown, including:

[0041] S1, cleaning and normalizing the obtained wind power data;

[0042] S2, build the RNN model, and determine the number of hidden layer nodes of the RNN model;

[0043] S3, performing a regression operation based on the obtained RNN model to obtain the predicted power, and constructing an objective function combining the minimum root mean square error of the predicted power and the measured power;

[0044] S4, initialize various parameters of the AFSA algorithm, and use the steps of the AFSA algorithm to optimize the weight of the RNN model;

[0045] S5, inputting the wind power data processed in step S1 int...

technical field

[0001] This application relates to the field of wind power, and in particular to an AFSA-GNN-based wind power prediction method. Background technique

[0002] There is a broad consensus on the development and utilization of renewable energy and the promotion of low-carbon green transformation of the economy. As a clean renewable energy, wind energy has been more and more widely used. However, due to the intermittence, volatility and randomness of wind energy itself, the output power of wind power fluctuates greatly and changes quickly. After wind power is connected to the grid, it will bring great challenges to the stable operation of the power system. Therefore, accurate prediction of wind power output can effectively improve the impact on the power system, and provide a basis for power dispatching departments to rationally arrange conventional energy generation and wind power generation, and to adjust power generation plans in a timely manner.

[0003] At ...

Examples

Embodiment Construction

[0039] In order to make the structure and advantages of the present application clearer, the structure of the present application will be further described below in conjunction with the accompanying drawings.

[0040] Specifically, the wind power prediction method based on AFSA-GNN proposed in the embodiment of this application, such as figure 1 shown, including:

[0041] S1, cleaning and normalizing the obtained wind power data;

[0042] S2, build the RNN model, and determine the number of hidden layer nodes of the RNN model;

[0043] S3, performing a regression operation based on the obtained RNN model to obtain the predicted power, and constructing an objective function combining the minimum root mean square error of the predicted power and the measured power;

[0044] S4, initialize various parameters of the AFSA algorithm, and use the steps of the AFSA algorithm to optimize the weight of the RNN model;

[0045] S5, inputting the wind power data processed in step S1 int...

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