Wind Power Prediction Model Based on Neural Network of PSO-BP
The model aims to improve the accuracy and stability of wind power prediction to cope with the uncertainty and volatility in wind farm operation.The principles of particle swarm optimization algorithm and back propagation algorithm are introduced,based on which the main factors affecting the output power of wind farms are analysed,the mathematical functional equation is constructed,and the functional relationship between wind power and related factors is resolved by multivariate linear fitting Design of the structure of BP neural network,and the PSO algorithm is used for the optimization of initial weights and thresholds of the neural network.It can more accurately fit the relationship between actual wind power and theoretical wind power.
wind power predictionparticle swarm optimisation algorithmback propagation algorithmmultivariate linear fitting
万方数据
维普
