融合多源异构气象数据的光伏功率预测模型

A Photovoltaic Power Prediction Model Integrating Multi-source Heterogeneous Meteorological Data

高精度光伏功率预测对提高电力系统运行效率具有重要意义。光伏功率受多种因素影响,其中云层的变化是最主要的不确定因素。传统光伏功率预测方法没有充分考虑云的3维结构和气象要素对光伏功率的影响。因此,该文提出一种融合多源异构气象数据的多源变量光伏功率预测模型(MPPM)。MPPM的核心包括时空条件扩散模型(STCDM)、注意力堆叠LSTM网络(ASLSTM)和多维特征融合模块(MFFM)。STCDM模型通过对2维卫星云图进行精确预测,消除了云层边界处的模糊现象。ASLSTM模型则提取了3维天气研究与预报模式(WRF)气象要素特征。MFFM模块将2维卫星云图特征和3维WRF气象要素特征进行融合,以得到未来1 h光伏功率预测结果。该文分别利用STCDM模型和MPPM模型开展卫星云图预测实验和光伏功率预测实验。实验结果显示,STCDM模型预测1 h内卫星云图的结构相似性指数(SSIM)达到0.914,MPPM模型预测1 h内光伏功率的相关系数(CORR)达到0.949,优于所有对比算法。

High-precision photovoltaic power prediction is of great significance for improving the operation efficiency of power system.Photovoltaic power is affected by many factors,among which cloud change is the most important uncertain factor.However,the traditional photovoltaic power prediction methods do not fully consider the influence of cloud three-dimensional structure and meteorological factors on photovoltaic power.To solve this problem,a Multi-source variables Photovoltaic power Prediction Model(MPPM)based on integrating multi-source heterogeneous meteorological data is proposed.The core of MPPM includes SpatioTemporal feature Conditional Diffusion Model(STCDM),Attention Stacked LSTM network(ASLSTM)and Multidimensional Feature Fusion Module(MFFM).STCDM accurately predicts the two-dimensional satellite cloud image,eliminating the blurring phenomenon at the cloud boundary.ASLSTM extracts the threedimensional Weather Research and Forecasting model(WRF)meteorological element features.MFFM fuses the two-dimensional satellite cloud image features and three-dimensional WRF meteorological element features to obtain the photovoltaic power prediction results for the next 1 h.In this paper,satellite cloud image prediction experiment and photovoltaic power prediction experiment are carried out by using STCDM model and MPPM model respectively.The results show that the Structural SIMilarity index(SSIM)of STCDM in satellite cloud image prediction within 1 h is up to 0.914,and the CORRelation index(CORR)of MPPM in photovoltaic power prediction within 1 h is up to 0.949,which are superior to all comparison algorithms.