Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach
Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach
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DOI：
10.1016/j.renene.2023.118952
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发表时间：
2023-06
期刊：
影响因子：
8.7
通讯作者：
Liwen Zhang;Robin Wilson;M. Sumner;Yupeng Wu
中科院分区：
文献类型：
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作者：
Liwen Zhang;Robin Wilson;M. Sumner;Yupeng Wu
云动力学是影响短期太阳辐照度间歇性变率的主要因素，从而影响太阳能发电场的产量。由于天空图像所包含的空间信息，它们已被广泛用于短期太阳辐照度预测，并取得了令人鼓舞的结果。目前，关于将图像与太阳辐射的定量测量相结合的最有前途的深度学习方法的讨论很少。为了解决这一差距，我们使用栅极架构优化了当前的主流框架，并提出了一种新的基于变压器的框架，试图获得更好的预测结果。研究发现，与基于后期特征级融合的经典CNN模型相比，基于早期特征级预测的变压器框架模型在2 min和6 min尺度上的斜坡事件平衡精度分别提高了9.43%和3.91%。然而，基于结果，可以得出结论，对于单幅图像-数字双峰模型，单幅图像的空间信息有效性很难达到10 min以上。这项工作有可能有助于基于天空图像的深度学习模型的可解释性和可迭代性。
Cloud dynamics are the main factor influencing the intermittent variability of short-term solar irradiance, and therefore affect the solar farm output. Sky images have been widely used for short-term solar irradiance prediction with encouraging results due to the spatial information they contain. At present, there is little discussion on the most promising deep learning methods to integrate images with quantitative measures of solar irradiation. To address this gap, we optimise the current mainstream framework using gate architecture and propose a new transformer-based framework in an attempt to achieve better prediction results. It was found that compared to the classical CNN model based on late feature-level fusion, the transformer framework model based on early feature-level prediction improves the balanced accuracy of ramp events by 9.43% and 3.91% on the 2-min and 6-min scales, respectively. However, based on the results, it can be concluded that for the single picture-digital bimodal model, the spatial information validity of a single picture is difficult to achieve beyond 10 min. This work has the potential to contribute to the interpretability and iterability of deep learning models based on sky images.