基于VMD与改进QRGRU的超短期风电功率概率预测

Ultra-short-term wind power probability prediction based on VMD and improved QRGRU

风电功率概率预测是分析未来风电功率不确定性的有效方法之一。为提高风电功率概率预测精度,文中提出基于变分模态分解(VMD)与改进门控循环单元分位数回归(QRGRU)的超短期风电功率概率预测方法。首先,采用VMD将原始风电功率序列分解成不同特征的模态函数;然后,对每个模态函数分别建立基于QRGRU的概率预测模型,并将变量间的网络结构约束作为目标函数的惩罚项,改进QRGRU权重在迭代修正过程中的平稳性;最后,在不同分位数条件下叠加各个模态函数预测值,并采用非参数核密度估计方法得到未来风电功率的概率密度函数。结合某风电场实测数据开展具体算例分析,结果表明所提方法能够兼顾区间覆盖率,减少区间宽度,在不同预测步长中均能表现较好的预测效果。

The probability prediction of wind power is an effective method to analyze the uncertainty of future wind power.To improve the accuracy of wind power probability prediction,a ultra-short-term wind power probability prediction method based on variational mode decomposition(VMD)and improved quantile regression gated recurrent unit(QRGRU)is proposed.Firstly,VMD is used to decompose the original wind power sequence into mode functions with different characteristics.Then,a probability prediction model based on QRGRU is established for each mode function.The network structure constraint is used as the penalty term of the objective function to improve the stability of the QRGRU weights in the iterative correction process.Finally,the predictive value of each mode function is superimposed under different quantile conditions,and the probability density function of future wind power is obtained by using a non-parametric kernel density estimation method.Based on the actual measurement data of a wind farm,a specific calculation example is analyzed.The results show that the proposed method can take the coverage of the interval into account,reduce the width of the interval and perform better predicting results in different prediction steps.