CAJ | 학술논문

备选风电场在寿命周期内的平均年发电量是风电场宏观选址的一个重要参考判据.为了提高风电场平均年发电量的预测精度，提出了一种基于风电场附近多个气象站长期测风数据的区域信息融合的平均年发电量预测方法.首先分别建立各气象站与风电场同期小时风速之间的相关模型，应用相关模型得到多个长期小时风速预测值，再用神经网络对长期小时风速预测值进行融合处理得出最终的小时风速预测值，在此基础上进行风电场平均年发电量的估算.仿真结果表明：本文提出的区域信息融合方法对年平均发电量的预测误差比采用单一气象站数据的预测误差最高可降低11.32%.
비선풍전장재수명주기내적평균년발전량시풍전장굉관선지적일개중요삼고판거.위료제고풍전장평균년발전량적예측정도，제출료일충기우풍전장부근다개기상참장기측풍수거적구역신식융합적평균년발전량예측방법.수선분별건립각기상참여풍전장동기소시풍속지간적상관모형，응용상관모형득도다개장기소시풍속예측치，재용신경망락대장기소시풍속예측치진행융합처리득출최종적소시풍속예측치，재차기출상진행풍전장평균년발전량적고산.방진결과표명：본문제출적구역신식융합방법대년평균발전량적예측오차비채용단일기상참수거적예측오차최고가강저11.32%.
Annual energy output of a candidate site in its life span is an important reference criterion of wind farm macro siting.A regional information fusion method,which allows the use of multiple reference wheather stations with a long history of wind speed and wind direction measurements,was proposed to im-prove the annual energy output prediction accuracy.Firstly,the correlation model was established between the short-term wind data of a single reference wheather station and the candidate wind farm,and the multi-ple long-term wind speeds of candidate site based on different reference stations were predicted by using the model.Then,the multiple prediction results were integrated by neural network to obtain the final long-term hourly wind speed data,and the annual energy output was subsequently determined on the basis of the knowledge of these wind speeds.The simulation results show that,by using the proposed method, the error reduction up to 1 1.32% has been achieved in the relative error of the average annual power out-put,with respect to the case of using a single reference wheather station method.