基于改进粒子群优化神经网络算法的波浪捕获功率预测

传统BP神经网络算法应用于波浪发电系统捕获功率预测,易陷入局部最优和泛化能力不足,为此提出一种改进的粒子群优化神经网络算法,动态调整学习因子并添加变异算子。采用间接预测策略,搭建从波浪数据到波浪捕获功率的直驱式波浪发电系统模型;应用改进算法预测分析波浪历史数据,输入搭建模型,进而获得波浪捕获功率预测值。比较分析不同预测步数和不同算法的仿真结果可知,改进算法能有效克服传统算法不足,提高预测精度。

WAVE CAPTURE POWER FORECASTING BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION NEURAL NETWORK ALGORITHM

The traditional BP neural network algorithm is applied to the acquisition power prediction of wave power generation system,which is easy to fall into local optimum and insufficient generalization ability.Therefore,an improved particle swarm optimization neural network algorithm is proposed to dynamically adjust the learning factor and add mutation operators.Using indirect prediction strategy to build a direct-drive wave power generation system model from wave data to wave capture power,applying an improved algorithm to predict and analyze the wave history data,input the construction model,and obtain the wave capture power prediction value.Comparing and analyzing the simulation results of different prediction steps and different algorithms,it is found that the improved algorithm can effectively overcome the shortcomings of traditional algorithms and improve the prediction accuracy.