一个基于自适应拉普拉斯协调增强的跨功能U-Net的云检测网络
Kaizheng Wang1, Ruohan Zhou1, Jian Wang1
1Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, P.R. China.
International journal of neural systems
|December 14, 2024
概括
准确的云检测对于太阳能预测至关重要. 一种新方法,ALCU-Net,增强了云识别,改善了光伏发电预测.
科学领域:
- 大气科学 大气科学
- 可再生能源技术可再生能源技术
- 计算机视觉 计算机视觉
背景情况:
- 云层覆盖的变化显著影响太阳辐射和光伏 (PV) 输出功率.
- 精确检测薄,碎片化云对于可靠的光伏电力预测至关重要.
研究的目的:
- 引入一种新的云检测方法,ALCU-Net,以提高光伏发电预测的准确性.
- 通过专门的模块来增强U-Net架构,以更好地提取云功能和空间连贯性.
主要方法:
- 开发了自适应拉普拉斯协调增强跨特征U-Net (ALCU-Net).
- 集成的自适应特征协调 (AFC),多粒度拉普拉西安增强 (MLE) 功能和交叉特征融合检测 (CCFE) 模块.
- 增强了传统的U-Net,增强了空间连贯性,层次特征集成和精细的边缘检测.
主要成果:
- 与现有的云检测方法相比,ALCU-Net表现出卓越的性能.
- 在识别厚云和薄云方面取得了很高的准确性.
- 成功地在不同的环境 (海洋,极地,海洋和陆地混合物) 中绘制了碎片化的云块.
结论:
- 在太阳能应用中,ALCU-Net在云检测方面取得了重大进展.
- 该方法在各种环境中的稳定性使其适用于现实世界的光伏预测.
- 改进的云检测准确度直接转化为更可靠的光伏发电预测.
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