基于优化变分模态分解的光伏功率超短期区间预测方法

ULTRA SHORT TERM INTERVAL PREDICTION METHOD OF PHOTOVOLTAIC POWER BASED ON OPTIMAL VARIATIONAL MODE DECOMPOSITION

针对城市分布式光伏电站在进行超短期功率预测时所需气象资料难以获取,在转折天气下光伏出力不确定性增加的问题,提出一种光伏功率超短期区间预测模型。首先该模型采用麻雀算法优化变分模态分解(VMD),在不同天气下将历史光伏出力分解成多个时序特征强的子模态;其次,通过长短期记忆神经网络LSTM对各子模态分别预测;再次,将各子模态的点预测结果叠加;算例验证结果表明:在各类天气条件下,所提模型相比于单纯使用气象因子的预测方法,具有更高的预测准确度和更强的适应性,同时也能在点预测的基础上提供较为准确的置信区间。

To address the challenges faced in obtaining accurate meteorological data,and increasing uncertainty of photovoltaic power output during transitional weather,an ultra-short term interval prediction model for photovoltaic power was proposed.The methodology leverages the Sparrow algorithm to optimize variational mode decomposition(VMD),which decomposes historical PV output into multiple sub-modes with strong temporal characteristics across different weather conditions.Secondly,each submode is predicted by LSTM,and the point prediction results are combined by superimposition.Finally,kernel density estimation was used to construct the error model and obtain ultra-short term interval prediction results for photovoltaic power.Simulation results illustrate that in all kinds of weather conditions,the proposed model has higher prediction accuracy and stronger adaptability than the prediction method using only meteorological factors,and can provide more accurate confidence intervals on the basis of point prediction.