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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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基于Xception和VGG19双层网络算法的光伏资源评估.

Lifeng Li1, Zaimin Yang1, Xiongping Yang2

  • 1Energy Development Research Institute, China Southern Power Grid, China.

Heliyon
|November 29, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的双层网络算法,将Xception和VGG19卷积神经网络结合起来,用于增强光伏 (PV) 资源评估. 与现有方法相比,新方法显著提高了评估光伏资源的准确性.

关键词:
卷积神经网络是一种卷积神经网络.双层网络算法 双层网络算法太阳能资源评估 太阳能资源评估双层网络框架 双层网络框架

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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
  • 太阳能资源评估 太阳能资源评估

背景情况:

  • 全球对新能源的需求凸显了可再生能源的重要性.
  • 光伏 (PV) 能源是可持续发电的一个重点.
  • 由于单一的框架,目前的光伏资源评估方法缺乏足够的准确性.

研究的目的:

  • 开发一种更准确的光伏资源评估方法.
  • 解决现有的单框架算法的局限性.
  • 提高光伏能源评估的可靠性和准确性.

主要方法:

  • 开发了一种新的两层网络算法,集成Xception和VGG19卷积神经网络.
  • 该算法是在双层网络框架内实现的.
  • 为了验证拟议方法的性能,进行了模拟.

主要成果:

  • 拟议的双层网络算法在光伏资源评估中显示出更高的准确性.
  • 结合Xception和VGG19方法的性能优于现有的评估算法.
  • 该研究验证了新框架的可行性和可靠性.

结论:

  • 开发的双层网络算法在光伏资源评估准确性方面取得了重大进展.
  • 这种人工智能驱动的方法为评估光伏能源潜力提供了更可靠的方法.
  • 这些发现支持采用先进的深度学习技术来评估可再生能源资源.