基于模糊信息粒化的光伏出力区间预测

PV power interval prediction based on fuzzy information granulation

相对于传统的光伏点预测而言,光伏区间预测可以为电网调度人员提供更加全面、有效的预测信息。鉴于此,文章提出一种基于模糊信息粒化理论的区间预测方法。针对光伏功率原始数据的强波动特性,采用集成经验模态分解方法将其分解为若干个子序列。并依据样本熵理论,将复杂度较高的子序列重组为随机分量,代表光伏输出的波动性,论文对该随机分量进行模糊化处理,从而得出其波动趋势以及波动上、下界,再分别进行预测;而复杂度相对较低的其他子序列代表光伏出力稳定分量,因此,直接对其进行确定性预测。论文采用经过纵横交叉算法改进的人工神经网络(CSO-BP)进行预测,得出最终光伏区间预测结果。

Compared with the traditional PV deterministic point forecast,PV interval prediction can provide more comprehensive and effective forecast information for grid dispatcher.Therefore,the paper proposes an interval prediction method based on fuzzy information granulation theory.In view of the fluctuating character of the photovoltaic power,the original PV data is decomposed into several sub-sequences by using the ensemble empirical model decomposition(EEMD).According to the sample entropy theory,the sub-sequences with higher complexity are reorganized into random components,which represent the volatility of PV output.This paper conducted the random components with fuzzy information granulation,which provided its fluctuating trend,fluctuating upper bound and lower bound.And the remaining sub-sequences with relatively small complexity represent the PV stabilized components,and therefore,the deterministic predictions are made directly.In this paper,the artificial neural network model(CSO-BP),which is improved by crisscross algorithm(CSO),is used to predict the PV interval prediction results.