Ultra-short Term Wind Power Prediction Based on Evolutionary Bagging Ensemble Learning
In order to achieve high-precision ultra-short-term prediction of wind power,this study conducted cross-domain feature selection based on Wasserstein distance and Random Forest(RF),and combined it with Evolutionary Bagging(EvoBagging).A new method for ultra-short term wind power prediction is proposed.Firstly,the Local Outlier Factor(LOF)algorithm is used for outlier detection,and K-Nearest Neighbors InterpolationK-NNI is used to replace outlier points in the original data.Secondly,the data after outliers were decomposed by Empirical Mode Decomposition(EMD)algorithm and statistically calculated to build the reconstructed data,and Wasserstein distance and RF cross-domain feature selection were used to reduce the feature dimension of the reconstructed data.Finally,in order to combine the advantages of each model to improve the prediction accuracy of the model,It is constructed with Deep Belief Network(DBN),Deep Neural Networks(DNN),Light Gradient Boosting Machine(LGBM)and eXtreme Gradient EvoBagging ensemble learning ultra-short term wind power prediction model based on Boosting(XGBoost)learner.It is proved that the prediction error of this model is reduced by 5％on average compared with that of a single model,and it can achieve high precision prediction of ultra-short term wind power.
万方数据
维普
