计及预测误差时空-条件相依特性的日内光伏出力区间预测方法

Intraday Photovoltaic Output Interval Prediction Method Considering the Spatiotemporal-Conditional Dependence of Prediction Error

由于光伏出力预测误差无法避免,区间预测可以更准确地描述光伏的不确定性,从而对电力系统的决策提供指导,而现有研究方法不能够充分地挖掘光伏功率的物理变化过程,因此提出了一种考虑预测误差时空-条件相依特性的日内光伏出力区间预测框架。首先通过外观相似性更新(appearance similarity update,ASU)模型得到考虑时间相依性的预测误差,再通过长短期记忆(long short-term memory,LSTM)网络模型以及空间相关性的分析得到考虑空间相依性的预测误差,并对预测的出力进行修正,最后依据其误差的条件相依性得到不同置信度下的区间预测。整体框架的效果在新疆光伏电场被验证,其均方根误差能够降低3%以上,同时考虑更新后的预测误差时空-条件相依性的区间预测效果有所提升,验证了所提方法的有效性和可行性。

Because the prediction error of photovoltaic point can not be avoided,interval prediction can be used to describe the uncertainty of photovoltaic more accurately,which can provide guidance for the decision-making of power system,but the existing research methods can not fully mine the physical change process of photovoltaic power.A prediction framework of intraday photovoltaic output interval considering the spatiotemporal-conditional dependence of prediction error is proposed.Firstly,the predic⁃tion error considering time dependence is obtained by appearance similarity update(ASU)model,then the prediction error consider⁃ing spatial dependence is obtained by long short-term memory(LSTM)model and spatial correlation analysis,and the prediction output is modified.Finally,the interval prediction under different confidences is obtained according to the conditional dependence of the error.The effect of the whole framework has been verified in a photovoltaic electric field in Xinjiang,and its root mean square error can be reduced by more than 3%.At the same time,the interval prediction effect considering the spatiotemporal-conditional dependence of the updated prediction error has been improved,which verifies the effectiveness and feasibility of the proposed method.