深度FS:对于表面太阳辐射的深度学习方法
Fatih Kihtir1, Kasim Oztoprak1
1Department of Computer Engineering, Konya Food and Agriculture University, Konya 42080, Turkey.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
准确预测表面阳光照射对于太阳能和天气预报至关重要. 一种新的深度学习方法,Deep-FS,结合卷积神经网络 (CNNs),显著改善了全球水平辐射 (GHI) 预测的传统方法.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 可再生能源可再生能源是可再生能源.
背景情况:
- 气候变化带来了重大的环境挑战,其中极端的气候变化是主要的驱动因素.
- 准确预测太阳辐射,特别是全球水平辐射 (GHI),对于太阳能应用和气象预报至关重要.
- GHI受大气条件,地理和时间动态的影响.
研究的目的:
- 通过先进的深度学习引入一种用于估计表面阳光照射的新方法.
- 使用NASA SORCE数据集验证拟议的方法.
- 提高太阳辐射预测的准确性,以改善太阳能和预测应用.
主要方法:
- 使用深度学习方法Deep-FS进行特征提取,以确定表面暴露的最相关预测因素.
- 时间序列分析使用卷积神经网络 (CNN) 进行预测.
- 使用标准绩效指标对传统方法进行验证和比较.
主要成果:
- 深度FS方法成功地提取了关键特征,这些特征对于准确的预测至关重要.
- 在太阳辐射预测的时间序列分析中,CNN表现出卓越的性能.
- 提出的深度学习方法在标准性能指标上显著超过了传统方法.
结论:
- 开发的深度学习方法,集成Deep-FS和CNNs,为估计表面阳光照射提供了一个非常有效的方法.
- 与现有技术相比,该新方法在全球水平辐射 (GHI) 预测中提供了更高的准确性.
- 这一进步具有优化太阳能利用和气象预报的巨大潜力.
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