Short-term wind speed prediction method, device, apparatus, system and storage medium

Abstract

A method for predict short-term wind speed the parameters of neural network are optimized by crossover algorithm, By means of iterative longitudinal and transverse cross-calculations of wind velocityparticles, the most suitable particles were screened out, Fitness reflects the difference between the target output and the actual output, the higher the fitness, the stronger the prediction ability of neural network, the particles with the highest fitness can embody the optimal solution of the whole training wind speed group, The particle with the highest fitness can not only find the optimal solution for the whole, It avoids the defect of the local optimization of the neural network, and can improve the generalization ability of the neural network by adjusting the parameters of the neural network with the highest fitness, the network parameters are better, the prediction accuracy can be greatly improved, and then the utilization of wind energy can be improved. The invention also discloses a short-term wind speed prediction device, a device, a system and a readable storage medium, which have the beneficial effects.

Description

technical field [0001] The present invention relates to the field of wind energy, in particular to a short-term wind speed prediction method, device, equipment, system and a readable storage medium. Background technique [0002] In recent years, due to the over-exploitation and over-use of non-renewable resources, the society is increasingly concerned about the rapid deterioration of the global climate situation. Many countries and cities around the world have begun to switch from traditional energy to renewable energy production. Among all renewable energy sources, wind energy has become the most concerned and fastest growing point in the world. [0003] Due to the characteristics of randomness, intermittency and volatility of wind power, large-scale wind power grid integration has a serious impact on the stability of the power system, such as increasing the operating cost of the power system and spinning reserve, etc. Connecting to the traditional power grid brings sever...

AI Technical Summary

Problems solved by technology

[0005] However, the parameters of the current conventional neural network model tend to fall into local optimum, that is, it is easy to be trapped in a limited space to seek the optimal solution, but cannot find a wide range of optimal parameters, the trained model is unstable, and the accuracy of the prediction result is low. lower

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