CPVPD-2024:通过拓增强深度学习框架获得的中国光伏电站数据集
Yang Yang1, Shaofu Lin2, Rongtian Lu1
1College of Computer Science, Beijing University of Technology, Chaoyang District, Beijing, 100124, China.
Scientific data
|October 1, 2025
概括
中国中国中国中国.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 地理空间科学 地理空间科学
- 遥感 遥感 遥感 遥感
背景情况:
- 中国光伏 (PV) 市场的快速增长对于能源转型和碳目标至关重要.
- 现有的光伏数据集受到碎片化和不一致的影响.
- 为了有效的光伏部署和管理,需要准确的空间数据.
研究的目的:
- 为中国开发一个全面而准确的国家光伏发电厂矢量数据集.
- 解决当前光伏数据集中的数据碎片化和不一致问题.
- 为光伏地点选择,生态评估和人工智能驱动的分析提供可靠的资源.
主要方法:
- 开发了2024年中国光伏发电厂矢量数据集 (CPVPD-2024).
- 使用了具有地理空间验证的深度语义细分框架 (DSFA-SwinNet).
- 覆盖了中国所有34个省级行政区.
主要成果:
- 实现了高精度,精度为90.38%和81.78%的交集在联盟 (IoU) 上.
- 在识别光伏阵列缺口和小型分布式发电厂方面取得了显著的改进.
- 到2024年,总装机光伏面积达到4,520.47平方公里,干旱地区的农电系统占主导地位.
结论:
- CPVPD-2024是第一个国家面板级光伏载体数据集.
- 该数据集使精确的光伏地点选择和生态评估成为可能.
- 为中国的能源转型提供先进的人工智能驱动的遥感分析.
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