一种参数自调整风电功率预测模型

为了提高风电功率预测精度,提出一种参数自调整风电功率预测模型.通过加权递推最小二乘(SWWRLS)方法建立预测模型,侧重当前数据对预测结果的影响,排除了历史数据对预测结果的干扰.模型通过加权递推的方法节省了存储空间,并且提高了模型对外界环境数据变化的自适应性.最后,分别采用支持向量机(SVM)方法、卡尔曼滤波(KF)方法和本文SWWRLS方法,以辽宁省某风电场的真实历史数据进行风电功率预测对比实验,实验结果表明,本文方法建立的模型具有较高的预测精度.

A Parameter Self-tuning Model for Wind Power Prediction

To improve the prediction accuracy of wind power， a parameter self-tuning model is proposed. The sliding window weighted recursive least square (SWWRLS) method is used to establish the wind power prediction model， which emphasizes most recent data in the prediction， ruling out the interference of historical data. Less memory space is used in the weighted recursive model， and the adaptability of the prediction model to the external environment data is increased. Finally， the simulation is carried out on the real historical data of the wind farm in Liaoning with support vector machine， Kalman filter and SWWRLS methods， separately， and the simulation results show the superiority of the proposed method.