基于高斯过程回归和粒子滤波的短期风速预测

SHORT-TERM WIND SPEED FORECASTING BASED ON GAUSSIAN PROCESS REGRESSION AND PARTICLE FILTER

建立高斯过程回归和粒子滤波相结合的短期风速预测模型,实现对历史风速序列异常值的在线动态检测与修正。首先,在训练样本集中通过高斯过程回归建立状态空间方程,采用粒子滤波算法对当前量测值进行状态估计,对估计值和量测值的残差进行分析,并根据"3σ"原则判断异常值。其次,修正异常值,并对修正后的风速序列重新建立高斯过程回归预测模型。在进行提前15分钟风速预测时,同样采用粒子滤波算法对最新量测值进行状态估计,实现了异常值在线检测并修正。算例分析结果表明,粒子滤波算法能够有效检测出异常风速值,降低了风速预测误差,提前15分钟风速预测时平均绝对百分比误差和均方根误差分别降至8.92%和0.5826 m/s。

Improving the wind speed prediction accuracy of wind farm will help enhance the power grid stability and economy. Noise or data loss often appears in historical wind speed sequences. These abnormal values will lead to inaccurate estimation of model parameters. Therefore,the affecting the prediction accuracy. Therefore,the detection and correction of abnormal value is the prerequisite and necessary measure to effectively analyze the law of wind speed. In this paper,a shortterm wind speed forecasting model combining Gaussian process regression and particle filter is established,which realizes online dynamic detection and correction of outliers. Firstly,the state space equation is established by the Gaussian process regression in the training sample set. The particle filter algorithm is then used to estimate the current measurement value.The residuals between the estimated and measured values are analyzed and the anomalous values are detected according to the principle of" 3σ ". Secondly,after the anomaly being corrected,the Gaussian process regression forecasting model is reconstructed. The particle filter algorithm is repeatedly used to estimate the latest measurement value during the process of15 mins ahead wind speed forecasting,realizing the online dynamic detection and correction of outliers. The case study show that the particle filter algorithm can effectively detect the abnormal values and reduce the wind speed prediction error,the average absolute percentage error and root mean square error are reduced to 8.92% and 0.5826 m/s respectively when the wind speed is predicted 15 minutes ahead.