Kihtir Fatih, Oztoprak Kasim
Department of Computer Engineering, Konya Food and Agriculture University, Konya 42080, Turkey.
Sensors (Basel). 2024 Dec 18;24(24):8059. doi: 10.3390/s24248059.
Contemporary environmental challenges are increasingly significant. The primary cause is the drastic changes in climates. The prediction of solar radiation is a crucial aspect of solar energy applications and meteorological forecasting. The amount of solar radiation reaching Earth's surface (Global Horizontal Irradiance, GHI) varies with atmospheric conditions, geographical location, and temporal factors. This paper presents a novel methodology for estimating surface sun exposure using advanced deep learning techniques. The proposed method is tested and validated using the data obtained from NASA's Goddard Earth Sciences Data and Information Services Centre (GES DISC) named the SORCE (Solar Radiation and Climate Experiment) dataset. For analyzing and predicting accurate data, features are extracted using a deep learning method, Deep-FS. The method extracted and provided the selected features that are most appropriate for predicting the surface exposure. Time series analysis was conducted using Convolutional Neural Networks (CNNs), with results demonstrating superior performance compared to traditional methodologies across standard performance metrics. The proposed Deep-FS model is validated and compared with the traditional approaches and models through the standard performance metrics. The experimental results concluded that the proposed model outperforms the traditional models.
当代环境挑战日益严峻。主要原因是气候的急剧变化。太阳辐射预测是太阳能应用和气象预报的关键环节。到达地球表面的太阳辐射量（全球水平辐照度，GHI）会随大气条件、地理位置和时间因素而变化。本文提出了一种使用先进深度学习技术估算地表太阳照射量的新方法。所提出的方法使用从美国国家航空航天局戈达德地球科学数据和信息服务中心（GES DISC）获取的名为太阳辐射与气候实验（SORCE）数据集的数据进行测试和验证。为了分析和预测准确数据，使用深度学习方法深度特征选择（Deep-FS）提取特征。该方法提取并提供了最适合预测地表照射量的选定特征。使用卷积神经网络（CNN）进行时间序列分析，结果表明在标准性能指标方面，与传统方法相比具有卓越性能。通过标准性能指标对所提出的深度特征选择（Deep-FS）模型进行验证，并与传统方法和模型进行比较。实验结果表明，所提出的模型优于传统模型。
