Ultra-short-term Photovoltaic Power Prediction Method Based on Spatio-temporal Feature Enhancement of Ground-based Cloud Images

Authors

• Zhiwei Kou, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China; Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot 010080, China; Key Laboratory of Smart Control for New Energy Power System of Inner Mongolia Autonomous Region, Hohhot 010080, China
• Fengyue Xin, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China
• Xiaoming Cui, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China; Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot 010080, China
• Yu Yin, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China; Key Laboratory of Smart Control for New Energy Power System of Inner Mongolia Autonomous Region, Hohhot 010080, China
• Feifan Li, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China
• Yongsheng Qi, College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China; Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot 010080, China; Key Laboratory of Smart Control for New Energy Power System of Inner Mongolia Autonomous Region, Hohhot 010080, China

Abstract

Abstract: In view of the time lag in ground-based cloud image acquisition and the insufficient accuracy of existing photovoltaic power prediction models, an ultra-short-term photovoltaic power prediction method named spatial-temporal feature enhancement-ground-based cloud image-improved LSTM (STFE-GCI-ILSTM), which is based on spatio-temporal feature enhancement-ground-based cloud images-improved long short-term memory (LSTM) network was proposed. A ground-based cloud image prediction model with multi-scale spatio-temporal feature enhancement (STFE-GCI) was constructed. By spatial feature enhancement, temporal feature enhancement, and multi-scale feature fusion technologies, the deep spatio-temporal features of historical cloud image sequences were extracted to generate future predicted cloud image sequences, thereby eliminating the time lag effect of data acquisition. CNN was used to extract the features of predicted cloud images. An improved LSTM network introducing time series segmentation and residual connections was constructed, and a nonlinear mapping model from cloud image features to photovoltaic power was established. The proposed method was verified based on the SKIPPD dataset under two typical weather scenarios: sunny and cloudy days. The experimental results indicate that in the ground-based cloud image prediction task, the STFE-GCI model outperforms baseline models such as ConvLSTM and E3DLSTM in terms of image clarity and structural similarity, showing a stronger ability of spatio-temporal feature extraction; in the photovoltaic power prediction task, the STFE-GCI-ILSTM model exhibits higher prediction accuracy and stability under different weather conditions; its various error indicators are significantly reduced; the coefficient of determination R2 is effectively improved. This method effectively utilizes the spatio-temporal evolution information of cloud states, significantly improving the accuracy and robustness of ultra-short-term photovoltaic power prediction.

Recommended Citation

Kou, Zhiwei; Xin, Fengyue; Cui, Xiaoming; Yin, Yu; Li, Feifan; and Qi, Yongsheng (2026) "Ultra-short-term Photovoltaic Power Prediction Method Based on Spatio-temporal Feature Enhancement of Ground-based Cloud Images," Journal of System Simulation: Vol. 38: Iss. 7, Article 19.
DOI: 10.16182/j.issn1004731x.joss.25-0856
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss7/19

First Page

2068

Last Page

2081

CLC

TM615; TP391

Recommended Citation

Kou Zhiwei, Xin Fengyue, Cui Xiaoming, et al. Ultra-short-term Photovoltaic Power Prediction Method Based on Spatio-temporal Feature Enhancement of Ground-based Cloud Images[J]. Journal of System Simulation, 2026, 38(7): 2068-2081.

DOI

10.16182/j.issn1004731x.joss.25-0856