一个使用圆约束的CNN-LSTM模型,用于暂时一致的太阳位置估计
Mark Mpabulungi1, Kyeongmin Yu1, Hyunki Hong1
1College of Software, Chung-Ang University, Heukseok-ro 84, Dongjak-ku, Seoul, 06973, Republic of Korea.
Heliyon
|May 31, 2024
概括
这项研究引入了一个深度学习系统,可以准确地估计太阳的位置,即使有云层覆盖. 这种新的方法通过分析图像序列来改进太阳能和增强现实系统.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 可再生能源系统可再生能源系统
背景情况:
- 准确的太阳位置估计对于太阳能,天气预报和增强现实至关重要.
- 现有的基于图像的方法由于依赖单一图像而扎着云层覆盖.
- 部分或完全的太阳遮蔽显著降低了当前系统的性能.
研究的目的:
- 开发一个强大的深度学习系统,用于精确估计太阳位置.
- 克服单图像方法的局限性,特别是在封闭条件下.
- 为了提高太阳能和增强现实应用的可靠性.
主要方法:
- 一个深度学习模型利用空间,时间和几何特征.
- 基于ResNet的卷积网络用于从图像序列中提取空间特征.
- LSTM层处理时间亮度变化和圆约束,以实现自然太阳路径的坚持.
主要成果:
- 在各种数据集 (Sirta,Laval,定制) 上获得了0.98的R2评分.
- 在精度上至少比以前的方法高出0.1.
- 证明了估计太阳位置的能力,即使被遮住.
结论:
- 拟议的深度学习方法为太阳位置估计提供了卓越的准确性和稳定性.
- 该系统有效地处理部分和完全的太阳遮蔽.
- 实现了简化,仅用相机的天空成像系统,取代了复杂的传感器阵列.
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