Cloud Map Multi-Feature Extraction for Ultra-Short-Term Photovoltaic Power Forecasting

To address the reduced accuracy of photovoltaic power forecasting caused by the fluctuating characteristics of ultra-short-term photovoltaic output, this paper proposes an ultra-short-term photovoltaic power forecasting method that integrates static and dynamic features extracted from ground-based cloud images with deep learning. The proposed method employs distortion correction, histogram equalization, and other techniques for image quality enhancement. Static cloud-image features are extracted using a threshold segmentation algorithm based on the maximum inter-class variance method. To characterize the dynamic evolution of cloud clusters, an optical flow method is introduced to accurately capture their motion speed and direction. The extracted static and dynamic cloud features are then combined to form a fused dataset. Finally, an ultra-short-term forecasting model combining a convolutional neural network and Autoformer is developed. Comparative results under different weather conditions show that the incorporation of multiple cloud-image features significantly improves forecasting accuracy, thereby validating the effectiveness of multimodal data fusion and the proposed deep learning architecture.