基于PSO-BP神经网络的风电功率预测模型分析

Wind Power Prediction Model Based on Neural Network of PSO-BP

该模型旨在提高风电功率预测的准确性和稳定性,以应对风电场运行中的不确定性和波动性。介绍了粒子群优化算法、反向传播算法的原理,在此基础上分析影响风电场的输出功率的主要因素,构建数学函数式,通过多元线性拟合,来解析风电功率与相关因素之间的函数关系设计BP神经网络的结构,并利用PSO算法对神经网络的初始权值和阈值进行优化,使该模型能够更准确地拟合实际风电功率与理论风电功率关系。

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.