基于VMD和GP的短期风电功率置信区间预测

传统风电点预测算法无法对风机出力的不确定性、随机性、波动性做出定量描述,提出一种基于变分模态分解(VMD)和高斯过程(GP)的区间预测方法,其意义在于能预测一定置信度下的短期风电功率波动区间。该方法利用变分模态分解算法将风功率序列分解为一系列不同带宽的模态分量以降低其非线性,对全部子模态分别建立高斯过程模型,最后叠加每个子模态预测结果得到风功率的置信区间。算例结果表明,与其他常规分解算法相比,该组合模型可以有效提高预测精度和预测区间覆盖率,减小预测区间宽度,具有一定的实用价值。

Confidence interval prediction of short term wind power based on VMD and GP

A short-term wind power confidence interval prediction method based on variational mode decomposition (VMD) and Gaussian process (GP) is proposed to describe the randomness, fluctuation and uncertainty of wind power. Firstly, the wind power sequence is decomposed into a series of modes with different bandwidths to reduce its nonlinearity by using the variational mode decomposition algorithm (VMD). Then the Gaussian process regression model is established for all the sub-modes. Finally, the prediction results of each sub-mode are added to obtain the forecasting confidence interval of wind power. The results of the example show that compared with other conventional decomposition method, this combined model can effectively improve the prediction accuracy and prediction interval coverage, and reduce the prediction interval width, which has certain practical value.