CAJ | 학술논문

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。
문중제출일충신형회색신경망락우화조합적풍력발전량예측연구，장인공신경망락예측모형화회색예측모형유효결합，불부고필료풍력、풍향화온도등영향인소，이차장왕년풍력발전량적역사수거종합고필，결합량충예측우점，종이제고료예측적준학도병강저예측오차。산례결과증명，저충신형적회색신경망락우화조합예측치오차저우단일적회색예측혹신경망락예측。
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.