CAJ | 학술논문

为了提高风电场风速短期预测的精确性,本文提出了基于Elman神经网络的预测。首先求出风速时间序列的嵌入维数和延迟时间,进而对混沌风速时间序列进行相空间重构。然后利用Elman神经网络对相空间重构后的风速时间序列进行预测,预测结果表明基于Elman神经网络的预测效果满足了精度要求。本文同时运用BP神经网络进行预测。仿真结果表明,基于ELMAN神经网络的预测模型能够较为准确的进行短期风速的预测,具有很高的工程实际应用意义。
위료제고풍전장풍속단기예측적정학성,본문제출료기우Elman신경망락적예측。수선구출풍속시간서렬적감입유수화연지시간,진이대혼돈풍속시간서렬진행상공간중구。연후이용Elman신경망락대상공간중구후적풍속시간서렬진행예측,예측결과표명기우Elman신경망락적예측효과만족료정도요구。본문동시운용BP신경망락진행예측。방진결과표명,기우ELMAN신경망락적예측모형능구교위준학적진행단기풍속적예측,구유흔고적공정실제응용의의。
In order to improve the accuracy of short-term wind speed forecast,this paper proposes a Elman neural network model.Reconstruction the phase space of the chaotic wind speed time series by calculating the embedding dimension and the delay time of the wind speed time series.Then the Elman neural network model can be used to forecast the wind speed.The results show the Elman neural network model can meet the accuracy requirements.At the same time,this thesis will use the BP neural network prediction model to forecast the wind speed time series.The simulation results show that the Elman neural network prediction model can be a good short-term wind speed prediction model.So it can be widely used in engineering practice.