通过基于物理的深度学习模型对农业电力进行快速地面辐射计算
L Kurumundayil1, D Burkhardt2, L Gfüllner2
1Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany. leslie.lydia.kurumundayil@ise.fraunhofer.de.
Communications engineering
|October 7, 2025
概括
一个新的深度学习模型大大加快了农业电力系统的太阳能计算速度. 这种先进的替代模型可以实现更快,更有效的光伏 (PV) 追踪和系统优化.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 光伏技术 光伏技术
背景情况:
- 农电系统集成太阳能电池板和农业,需要精确的阳光分布分析.
- 精确的辐射量化对于优化双面太阳能模块性能和产量至关重要.
- 对于复杂的农电场景的传统射线追踪模拟是计算密集的.
研究的目的:
- 开发一个计算效率高的替代模型,用于计算农业电力系统的地面辐射.
- 为了使光伏 (PV) 追踪器算法能够实时进行辐射计算.
- 优化农业电压系统的设计和运行,以获得最大的作物和电力产量.
主要方法:
- 一个基于深度学习的代孕模型被训练,使用来自光线追踪模拟的物理信息数据.
- 该模型使用直接正常辐射,分散水平辐射,太阳位置和系统几何作为输入.
- 用3D场景编码的生成回归方法用于辐射映射.
主要成果:
- 替代模型在3毫秒内计算地面辐射图,比标准光线追踪速度提高了四个数量级.
- 实现了太阳光分布在地面和模块层面的快速,准确的计算.
- 在边缘计算应用中证明了飞行中的射线追踪的可行性.
结论:
- 深度学习替代模型为农业电力系统中的辐射计算提供了显著的加速.
- 这种方法促进了光伏系统的高效管理和优化,特别是在边缘计算应用中.
- 能够更快地开发和部署先进的光伏追踪算法.
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