Short-Term Wind Power Prediction Based on Feature Recombination and IQPSO-BILSTM-RF
Short term wind power prediction is crucial for the normal operation of the power system.In order to improve the accuracy of wind power prediction,a combination model of bidirectional long short-term memory network(BILSTM)and random forest(RF)is proposed based on feature recombination method and improved quantum particle swarm optimization algorithm(IQPSO)to optimize the short-term wind power prediction.Firstly,using local mean decomposition to process wind power data,multiple sub components are obtained,and their fuzzy entro-py is calculated to recombine new feature components.Secondly,using IQPSO optimized BILSTM to predict feature components,the results of each component are superimposed to obtain preliminary predicted values.Finally,error correction was performed on the preliminary predicted values using IQPSO optimized RF.The experiment showed that the coefficient of determination(R2)of the model reached 0.994 25,which is superior to other models.The ablation experiment verified the necessity of each module.
wind power predictionfeature recombinationimproved quantum particle swarm optimization algorithmbidirectional long short-term memory networkrandom foresterror correction
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