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Short Term Wind Power Forecasting Method Based on WDRI-RF Model

The ever-increasing number of wind power generation devices has increased the scale of wind power grid connection, which in turn caused fluctuations in the grid. The problem is largely solved by wind power forecasting algorithms. Wind Direction Relevance Improvement-Random Forest (WDRI-RF) is proposed to solve the problem that low relevance factors in traditional wind power forecasting are not highly utilized by the model. The problem of low relevance between wind direction factor and wind power is solved by the Wind Direction Relevance Improvement Algorithm. On the one hand, the Random Forest model is used to complete the multiple types of data generated by the above algorithm. On the other hand, it is used to solve the matching problem of data and model. The results of an example show that the Relevance of wind direction factors is significantly improved. In addition, compared with the traditional model, WDRI-RF is proposed in this paper can improve the prediction accuracy.