An improved BP neural network method for Wind Power Prediction

Abstract

For large wind farms with grid-connected power generation systems, power prediction is very important. A sparrow search algorithm is proposed for optimizing the wind power prediction in BP neural network to improve the accuracy and stability of wind power prediction. Tent chaotic mapping gets used to increase the species diversity of the sparrow search algorithm and improve the ability of the algorithm to step out of local optimization and global search. The improved SSA-BP algorithm and traditional BP neural network method are used to predict the wind power. The prediction results and errors of the two methods are compared. The comparative analysis of the actual measured data and model prediction data shows that the SSA-BP method can better track the trend of the actual wind speed data, reduce the error and improve the prediction accuracy.