采用k近邻进行空间相关性超短期风速预测

Ultra-short term wind speed prediction based on spatial correlation by k-nearest neighbor

提高超短期风速预测准确率和可靠性的途径之一,是从历史观测值中充分挖掘风速相关性的特征和规律。将本地最新的风速历史观测值结合按照最优延迟时间提前的上游风速观测值,形成空间相关性k近邻预测的参考矢量;以相关系数作为相关性的具体评价指标,从风速历史观测值中优选出该参考矢量的k个最相似的近邻;采用7种回归模型进行本地的未来风速预测。荷兰Huibertgat地区冬季风速预测的仿真结果表明:使用线性回归、偏最小二乘回归、最小二乘支持向量回归3个优化模型预测,优化的k近邻数量为100左右,优化的历史数据年数为10 a;空间相关性k近邻风速预测能够有效使用历史数据的相似性进行可靠的超短期风速预测。

One approach to improve the accuracy and reliability of ultra-short term wind speed prediction is to thoroughly exploit the characteristics and laws of wind speed correlation from historical observations. The reference vector of k-nearest neighbor prediction based on spatial correlation is formed by the combination of latest local wind speed historical observations and upstream wind speed observations adjusted by its optimal lag time. The correlation coefficient is taken as the concrete evaluation index,and k most similar neighbours of the reference vector are optimally selected from the wind speed historical observations. Seven regression models are adopted for the future local wind speed prediction. The simulative results of wind speed prediction of Huibertgat,Holland in winter show that the optimal number of k-nearest neighbours is about 100 and the optimal year number of historical data is 10 a by the prediction of three optimal models,i.e. linear regression,partial least squares regression and least squares support vector machine regression,and the proposed method can effectively use the similarity of historical data for reliable ultrashort term wind speed prediction.