BP neural network wind speed prediction method based on genetic algorithm optimization

A BP neural network and wind speed prediction technology, applied in neural learning methods, biological neural network models, genetic laws, etc., can solve the problems of difficulty in convergence of BP neural network training, affecting the efficiency and accuracy of prediction, and improve computing efficiency and efficiency. Accuracy, improved stability, and high search efficiency

Pending Publication Date: 2020-05-15
NANJING UNIV OF SCI & TECH
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Problems solved by technology

When encountering bad data, BP neural network training will become difficu

Method used

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  • BP neural network wind speed prediction method based on genetic algorithm optimization
  • BP neural network wind speed prediction method based on genetic algorithm optimization
  • BP neural network wind speed prediction method based on genetic algorithm optimization

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Experimental program
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Embodiment 1

[0077] In this embodiment, the daily hourly wind speed data of a wind farm in May is divided into training sample data and test sample data.

[0078] Step 1. Obtain the wind speed data of the wind farm, divide these sample data into a training sample set and a test sample set, and perform normalization processing, establish a BP neural network prediction model, and estimate the initial value range, as follows:

[0079] Step 1.1, input data: (Table 1-1,, Table 1-2, Table 1-3)

[0080] Table 1-1

[0081]

[0082] Table 1-2

[0083]

[0084] Table 1-3

[0085]

[0086] Step 1.2, construct BP neural network, estimate initial value range:

[0087] The input training sample data is selected according to the rolling method, that is, a total of 744 data of 24-hour wind speed in all 31 days are arranged in a group according to time sequence, and the wind speed of the next hour is predicted by the wind speed of every 5 adjacent hours , generating a total of 715 sets of trai...

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Abstract

The invention discloses a BP neural network wind speed prediction method based on genetic algorithm optimization. The method comprises the following steps: firstly, collecting wind speed data of a wind power plant, establishing a BP neural network prediction model, and estimating an initial value range; then, performing real number coding on the weight and the threshold of the neural network, randomly generating a group of initial individuals to form an initial population, and each initial individual represents an initial solution of a problem; calculating the fitness of each individual in thepopulation, performing selection, crossover and mutation operations to form a next generation of population, evaluating the fitness of the individuals in the new population, judging convergence conditions, selecting an optimal individual, and taking the optimal individual as an initial weight and a threshold of the neural network; and finally, training by utilizing matlab to obtain a wind speed prediction value. According to the method, the wind speed prediction efficiency and accuracy of the BP neural network are improved.

Description

technical field [0001] The invention relates to the technical field of wind speed prediction of wind farms, in particular to a BP neural network wind speed prediction method based on genetic algorithm optimization. Background technique [0002] With the development of economy and society, people's demand for clean energy is increasing. As a renewable clean energy, wind energy has great development potential. However, wind power generation is intermittent and time-varying, so it is particularly important to accurately predict wind speed. Accurate wind speed prediction is helpful to ensure the safe, stable and economical operation of the power system. [0003] The wind speed prediction model is mainly based on establishing a functional mapping relationship between future data and historical data, that is, predicting future output based on historical data. The artificial neural network has the ability of self-learning and self-organization. The learning and training of the art...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06N3/12G06Q10/04G06Q50/06
CPCG06N3/084G06N3/126G06Q10/04G06Q50/06G06N3/045
Inventor 李军潘茹悦
Owner NANJING UNIV OF SCI & TECH
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