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

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...

AI Technical Summary

Problems solved by technology

When encountering bad data, BP neural network training will become difficult to converge, greatly affecting the efficiency and accuracy of prediction

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