Abstract
Wind energy is a clean and pollution-free renewable energy source, but it has the characteristics of randomness and intermittentness. Therefore, it is of great economic significance and practical value to study the high-precision wind power prediction model for accurately predict the power generation of wind farms. This paper use the small world optimization algorithm (SWOA) to optimize the selection of the weights and threshold of the BP neural network, so that it has the advantages of small errors and global optimization. Then we proposes an improved BP neural network model based on the small world optimization algorithm. Afterwards, the model was applied to wind power prediction in actual wind fields. The experiment results show that SWOA can quickly and accurately find the global optimal solution of the parameters of the BP neural network model, which can further improve the SWOA-BP model to obtain better prediction performance.
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