基于局部高斯过程的短期风速预测

准确的风速预测对于风电场和电力系统的稳定运行具有重要意义。本文提出一种基于局部高斯过程的短期风速预测方法。首先，把总的训练样本集按固定长度的时间窗划分成许多个子训练集。然后，运用局部高斯过程模型对各个子训练集进行建模，通过最小化训练集的预测误差为优化目标，用改进粒子群算法求取模型的最优超参数。最后，对某实测风速数据进行风速预测分析，结果表明基于局部高斯过程的短期风速预测能有效提高风速预测精度。

Short-term Wind Speed Forecasting Based on Local Gaussian Process

Wind speed forecasting is very important to the operation of wind power plants and power system. A short-term wind speed forecasting method based on local Gaussian process model is proposed. Firstly, the training sample set is divided into many sub training set according to the fixed length of time window. Secondly, the local Gaussian process model is used to forecast the wind speed of each sub training set. By minimizing prediction error of the training set as the optimization goal, the improved PSO algorithm is used to optimize the hyper parameters. The prediction results show that the proposed method can improve the prediction accuracy.