Short-term Forecasting of Surface Solar Incident Radiation on Edge Intelligence Based on AttUNet
Journal of Cloud Computing(2024)
摘要
Solar energy has emerged as a key industry in the field of renewable energy due to its universality, harmlessness, and sustainability. Accurate prediction of solar radiation is crucial for optimizing the economic benefits of photovoltaic power plants. In this paper, we propose a novel spatiotemporal attention mechanism model based on an encoder-translator-decoder architecture. Our model is built upon a temporal AttUNet network and incorporates an auxiliary attention branch to enhance the extraction of spatiotemporal correlation information from input images. And utilize the powerful ability of edge intelligence to process meteorological data and solar radiation parameters in real-time, adjust the prediction model in real-time, thereby improving the real-time performance of prediction. The dataset utilized in this study is sourced from the total surface solar incident radiation (SSI) product provided by the geostationary meteorological satellite FY4A. After experiments, the SSIM has been improved to 0.86. Compared with other existing models, our model has obvious advantages and has great prospects for short-term prediction of surface solar incident radiation.
更多查看译文
关键词
Solar energy,Edge intelligence,Attention mechanism,AttUNet
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要