用于短期风速预测的优化核心向量回归模型

An optimized CVR model for short-term wind speed forecasting

风能的不确定性和难以准确预测给风电并入电网带来了困难。风速是影响风能的重要因素,风速的预测精度对风电功率预测的准确性有重要影响。提出一种优化的核心向量回归(CVR)模型,进行短期风速预测。其风速数据从某风电场每隔1 h采集1次,并采用粒子群优化(PSO)算法对CVR模型的参数进行优化,利用优化后的CVR模型进行风速预测。试验结果表明,在时空复杂度相当的情况下,该方法具有比CVR和SVR(support vector regression)更高的预测精度。

It is difficult to merge wind power into a grid,owing to wind power's uncertainty and prediction inaccuracy.Wind speed is an important factor affecting wind power,so the accuracy of wind speed prediction has a major impact on the wind power prediction.An optimized prediction model based on core vector regression(CVR) is proposed in short-term wind speed forecasting.The wind speed data from a wind farm are collected hourly as the inputs.The particle swarm optimization(PSO) method is used to optimize the CVR model parameters.Experimental results show that the method has higher prediction accuracy than the CVR and support vector regression(SVR) method.