基于核主成分分析和食肉植物算法优化随机森林的风电功率短期预测OA

In order to improve the accuracy of short-term wind power prediction,a short-term wind power forecasting method based on kernel principal component analysis and carnivorous plant algorithm(CPA)optimized random forest(RF)was proposed.Firstly,8 meteorological factors related to wind power were extracted from 13 meteorological factors by kernel principal component analysis,and then these 8 meteorological factors were input into the prediction model.Then,the carnivorous plant algorithm was used to optimize the random forest,and to construct the CPA-RF prediction model,which can solve the problem that the prediction accuracy of the RF prediction model is not high enough.Finally,The actual wind power data was selected for testing.The test results indicate that 8 meteorological factors which are extracted through kernel principal component analysis method,is used as input.The effect is better than that of 13 meteorological factors directly inputted.The CPA-RF prediction model with higher prediction accuracy,significantly outperforms LSTM prediction model as well as other comparable models including BiLSTM and RF prediction model.This method can provide a reference for accuracy improvement of the short-term wind power prediction.