基于MBI-PBI-ResNet的超短期光伏功率预测

为了增强光伏并网的稳定性，提高光伏发电功率预测精度，提出一种基于相似日聚类、群分解(swarm decomposition, SWD)和MBI-PBI-ResNet深度学习网络模型的光伏发电功率超短期预测方法。首先，使用快速傅里叶变换(fast fourier transform, FFT)提取太阳辐照度的期望频率，将其作为聚类特征向量，并根据此聚类特征向量采用自适应仿射传播聚类(adaptive affinity propagation clustering, AdAP)实现相似日聚类。其次，对每一类相似日分别使用群分解算法进行分解，以提取原始数据的多尺度波动规律特征。最后，利用MBI-PBI-ResNet来实现对天气环境多变量关联影响下的时序特征挖掘以及对多尺度分量的局部波形空间特征和长时间依赖时序特征的同时挖掘，并对不同类型特征进行综合集成来实现光伏发电功率超短期预测。研究结果表明：所提方法在光伏发电功率超短期预测领域相较于其他深度学习方法预测精度提高了3%以上，说明此方法在光伏发电功率超短期预测领域具有较高的预测精度和较强的泛化能力。

Ultra-short-term PV power prediction based on MBI-PBI-ResNet

To enhance the stability of photovoltaic (PV) grid connection and improve the accuracy of PV power prediction, an ultra-short-term prediction method of PV power based on similar day clustering, swarm decomposition (SWD) and MBI-PBI-ResNet network deep learning network model is proposed. First, the expected frequency of solar irradiance is extracted using a fast Fourier transform (FFT). This is used as a clustering eigenvector, and similar day clustering is achieved using adaptive affinity propagation clustering (AdAP) based on this clustering eigenvector. Second, each class of similar days is decomposed separately using swarm decomposition algorithms to extract the multi-scale fluctuation pattern features of the original data. Finally, MBI-PBI-ResNet is used to realize the mining of temporal features under the influence of multivariate correlation of weather environment. Also it is used for the mining of spatial features of local waveforms and long time-dependent temporal features of multiscale components at the same time, as well as the combined integration of different types of features to realize ultra-short-term prediction of PV power generation. The results show that this method improves the prediction accuracy by more than 3% compared with other deep learning methods in the field of ultra-short-term prediction of photovoltaic power. This indicates that this method has higher prediction accuracy and stronger generalizability in the field of ultra-short-term prediction of photovoltaic power.