基于新型相似日选取和VMD-NGO-BiGRU的短期光伏功率预测

Short Term Photovoltaic Power Prediction Based on New Similar Day Selection and VMD-NGO-BiGRU

光伏功率预测在现代电力系统调度和运行中起着重要作用.针对光伏发电功率的多变性和复杂性,提出了一种基于新型相似日选取和北方苍鹰算法(Northern Goshawk Optimization,NGO)优化双向门控循环单元(Bidirectional Gated Recurrent Unit,BiGRU)的短期光伏功率预测方法.首先,利用斯皮尔曼相关系数选取主要气象因子,通过变分模态分解(Variational Mode Decomposition,VMD)将原始光伏功率和最大气象因子分解重构为一系列子信号.其次,通过构建新的评价指标筛选出相似日数据集,利用一组BiGRU建立以相似日子信号为网络输入的深度学习模型,并利用NGO对每个BiGRU网络的超参数进行有效优化.最后,对各子信号的预测结果进行综合,得到最终的光伏功率预测值.仿真结果表明,所提混合深度学习方法在预测精度和计算效率方面均优于其他方法.

Photovoltaic power prediction plays an important role in the scheduling and operation of modern power systems.Aiming at the variability and complexity of photovoltaic power generation,a short-term PV power prediction method based on new similar day selection and northern Goshawk optimization(NGO)to optimize bidirectional gated recurrent unit(BiGRU)is proposed.The main meteorological factors are selected with the Spearman correlation coefficient,and the original PV power and maximum meteorological factor are decomposed into a series of sub-signals by variational mode decomposition(VMD).Then,according to the construction of new evaluation indicators,the data set of similar days is screened out,a group of BiGRU is used to establish a deep learning model with similar day signals as network input,and NGO is used to effectively optimize the hyperparameters of each BiGRU network.Finally,the predicted value of PV power is obtained by synthesizing the predicted results of each sub-signal.Simulation results show that the proposed hybrid deep learning method is superior to other methods in terms of prediction accuracy and computational efficiency.