Short-term Wind power Prediction Model Based on Cuckoo Algorithm and BP neural Network

Aiming at the shortcomings of existing accurate prediction methods of wind power generation, a prediction model combining cuckoo algorithm and BP neural network is proposed. The optimal solution obtained by the cuckoo algorithm is used to assign the initial weight and threshold value of the BP neural network, and the historical power generation data is trained, and then the wind power is predicted by the BP neural network. In this paper, the actual wind power generation in Belgium is taken as an example to compare and analyze the error between BP neural network based on cuckoo algorithm and common BP neural network, BP neural network based on particle swarm optimization algorithm and BP neural network based on Genetic Algorithm in wind power generation prediction. The experimental results show that the improved BP neural network model based on the cuckoo algorithm has better nonlinear fitting ability and higher accuracy than the other three network models for wind power prediction.