摘要
针对短期风速预测问题,提出一种基于人工蜂群算法(Artificial Bee Colony Algorithm,ABC)和BP(BackPropagation)神经网络的预测模型。将温度、当地气压、海平面气压、风向、风切变、风速等气象数据作为原始样本数据,首先进行归一化处理,然后利用BP神经网络对归一化后的数据进行训练,并用ABC优化BP的权值阈值矩阵,建立短期风速预测模型。仿真结果表明,与BP神经网络、ABC-SVM等模型进行对比,该模型在短期风速预测方面的准确度更高。
In order to predict the short term wind speed,an model based on Artificial Bee Colony algorithm(ABC)and BP(Back Propagation)neural network is proposed.The meteorological data,such as temperature,local pressure,sea level pressure,wind direction,wind shear and wind speed are taken as the original sample.Firstly,the data is normalized.Then using BP neural network to training the data,using ABC algorithm to optimize the weight and threshold matrix,the prediction model of the short term wind speed is set up.the simulation results show that,compared with the BP neural network and ABC-SVM model,the ABC-BP model is more accurate in the prediction of the short term wind speed.
作者
张旸
颜七笙
ZHANG Yang;YAN Qisheng(School of Science,East China University of Technology,330013,Nanchang,PRC)
出处
《江西科学》
2019年第1期26-31,共6页
Jiangxi Science
基金
江西省教育厅科技项目(No.GJJ150600)
江西省研究生教改项目(No.JXYJG-2012-059)
关键词
短期风速预测
人工蜂群算法
BP神经网络
支持向量机
short term wind speed prediction
artificial bee colony algorithm
BP neural network
support vector machine
