电力系统保护与控制2025，Vol.53Issue(11)：84-94,11.DOI:10.19783/j.cnki.pspc.241221
基于地基云图数据多维特征融合的光伏功率预测算法
Photovoltaic power prediction algorithm based on multidimensional features fusion of ground-based cloud images
摘要
Abstract
To address the limitations of traditional photovoltaic(PV)power prediction algorithms,particularly their inability to accurately capture cloud conditions and their low prediction accuracy,a PV power prediction algorithm based on the fusion of ground-based cloud images and dual-stream data is proposed.First,accurate cloud condition information from ground-based cloud images is utilized,and dense optical flow is employed to extract spatiotemporal and detail change features between adjacent image frames.Then,the advantages of convolutional neural network in feature extraction and residual network in suppressing information loss in model learning are combined to improve the learning ability of the prediction model on the long-term mapping relationship between PV power output and image data.In addition,an attention mechanism is introduced to compensate for the underutilization of critical information during model training.Experimental results indicate that integrating ground-based cloud images and optical flow data offers more spatiotemporal features under cloudy weather conditions.Compared with benchmark models,the proposed method reduces the root mean square error(RMSE)and the mean absolute error(MAE)by 15.50％and 11.65％under sunny conditions,and by 4.05％and 5.15％under cloudy conditions,respectively.This contributes to accurate and reliable forecasting of PV power output by effectively utilizing cloud motion information,thereby improving the timeliness and accuracy of scheduling operations in PV power stations.关键词
深度学习/功率预测/地基云图/注意力机制/稠密光流算法Key words
deep learning/power prediction/ground-based cloud mapping/attention mechanism/dense optical flow algorithm引用本文复制引用
吐松江·卡日,吴现,马小晶,雷柯松,余凯峰,司伟壮..基于地基云图数据多维特征融合的光伏功率预测算法[J].电力系统保护与控制,2025,53(11):84-94,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.52067021). 国家自然科学基金项目资助(52067021) （No.52067021）
新疆维吾尔自治区自然科学基金面上项目资助(2022D01C35) （2022D01C35）
新疆维吾尔自治区优秀青年科技人才培养项目资助(2019Q012) （2019Q012）
