基于灰色神经网络优化组合的风力发电量预测研究  

Study on Wind Power Capacity Prediction Based on the Optimal Combination of the Grey Neural Network

作　　者：章勇高[1] 王妍[1] 孙佳[2] 高彦丽[1] 

机构地区：[1]华东交通大学电气与电子工程学院,南昌330013 [2]南昌大学信息工程学院自动化系,南昌330031

出　　处：《电测与仪表》2014年第22期30-34,共5页Electrical Measurement & Instrumentation

基　　金：江西省教育厅科技项目(GJJ14387);江西省科技厅科技攻关项目(20122BBF60084)

摘　　要：文中提出一种新型灰色神经网络优化组合的风力发电量预测研究,将人工神经网络预测模型和灰色预测模型有效结合,不仅考虑了风力、风向和温度等影响因素,而且将往年风力发电量的历史数据综合考虑,结合两种预测优点,从而提高了预测的准确度并降低预测误差。算例结果证明,这种新型的灰色神经网络优化组合预测值误差低于单一的灰色预测或神经网络预测。This paper proposed a study on wind power capacity prediction based on the optimal combination of the grey neural network,which combined the artificial neural network（ ANN）prediction model with the grey prediction model effectively. This study not only considered such factors as wind velocity,wind direction and temperature,but also took into account the historical data of the wind power capacity in the previous years. The combination of the advantages of both predictions improved the prediction accuracy and reduced the prediction errors. The results of the calculation ex-ample proved that the forecasting value error of the grey neural network optimal combination was lower than that of the single grey prediction or neural network prediction.

关 键 词：人工神经网络 灰色预测技术 优化组合预测技术 误差 风力发电量 

分 类 号：TM93[电气工程—电力电子与电力传动]