基于模拟退火粒子群算法的风电场短期风速预测  

Short-term wind speed forecast based on simulation annealing particle swarm optimization algorithm

作　　者：程加堂[1] 熊燕[1] 罗瑞[1] 

机构地区：[1]红河学院工学院,云南蒙自661199

出　　处：《可再生能源》2014年第4期485-488,共4页Renewable Energy Resources

基　　金：云南省教育厅科学研究基金项目(2012Y450)

摘　　要：针对风电场短期风速预测存在精度较低的问题,引入了一种基于灰色模型与模拟退火粒子群优化BP神经网络(SAPSO-BP)相互嵌入而成的预测模型。该方法在SAPSO-BP网络的输入层前增加一个灰化层,在网络输出层后增加一个白化层,以改进网络的拓扑结构,提高模型的容错能力。仿真试验结果表明,该预测模型具有较好的鲁棒性,其平均绝对误差及均方误差分别为18.7%和5.11%,可用于风电场短期风速的预测。Aiming at low prediction accuracy of short-term wind speed, a forecasting method of grey model and simulated annealing particle swarm optimization BP neural network （SAPSO-BP） embedded in each other was proposed. The method is to optimize the topological structure of the SAPSO-BP network by adding a grey layer before the input layer and a white layer after the output layer so as to enhance the fault-tolerant ability. The simulation results show that the mean absolute error, mean squared error of forecasting are 18.7% and 5.11%; the model has better robustness,and can be used for short-term wind speed prediction.

关 键 词：风电场 短期风速预测 灰色模型 模拟退火粒子群算法 BP网络 

分 类 号：TK89[动力工程及工程热物理—流体机械及工程] P425.2[天文地球—大气科学及气象学]