Abstract
Wind power generation is currently one of the most promising power generation technologies. It is particularly important to improve the prediction accuracy of wind power output, which can effectively reduce the impact on the grid when wind power is connected to the grid. Based on the fractal model, this paper integrates it with the wind power prediction model, and combines the custom K nearest neighbor algorithm to evaluate the prediction effect using multi-dimensional indicators. Finally, taking the data of a wind farm in northwest china as an example, compared it with the prediction model of random forest, support vector machine and gradient boosting decision tree prediction model to verify the effectiveness of the prediction algorithm in this paper.
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