Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNet

Mengmeng Cui, Shizhong Zhao, Jinfeng Yao
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Abstract

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.
基于 AttUNet 的边缘智能地表太阳入射辐射短期预报
太阳能因其普遍性、无害性和可持续性,已成为可再生能源领域的重要产业。准确预测太阳辐射对于优化光伏电站的经济效益至关重要。在本文中,我们提出了一种基于编码器-翻译器-解码器架构的新型时空注意力机制模型。我们的模型建立在时空 AttUNet 网络的基础上,并加入了辅助注意力分支,以增强从输入图像中提取时空相关信息的能力。并利用边缘智能的强大能力实时处理气象数据和太阳辐射参数,实时调整预测模型,从而提高预测的实时性。本研究使用的数据集来自地球静止气象卫星 FY4A 提供的地表太阳总入射辐射(SSI)产品。经过实验,SSIM 已提高到 0.86。与其他现有模型相比,我们的模型具有明显优势,在地表太阳入射辐射短期预测方面具有广阔前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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