基于转移学习方法的天空图像分类
Ruymán Hernández-López1, Carlos M Travieso-González1, Nabil I Ajali-Hernández1
1Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
由于天气多云,太阳能发电的准确预测受到阻碍. 这项研究使用卷积神经网络 (CNN) 来分类天空图像,通过EfficientNet模型实现98.09%的准确性,以便更好地预测可再生能源.
科学领域:
- 计算机科学 计算机科学
- 可再生能源系统可再生能源系统
- 大气科学 大气科学
背景情况:
- 精确预测光伏 (PV) 发电对于电网稳定性和管理至关重要.
- 阴天会对当地太阳能发电量产生重大影响,这对实时能源管理构成了挑战.
- 实时天空状况评估对于优化独立光伏系统运行和管理能源消耗与发电至关重要.
研究的目的:
- 评估深度学习模型对分类天空图像的有效性,以预测当地天气状况.
- 在可再生能源预测的背景下,确定最准确的卷积神经网络 (CNN) 架构用于天空图像分类.
- 利用转移学习 (TL) 技术来提高天空图像分类模型的性能.
主要方法:
- 利用卷积神经网络 (CNN) 和转移学习 (TL) 来进行天空图像分类.
- 测试了EfficientNet家族的各种架构和两个ResNet模型.
- 应用交叉验证方法,在不同的实验设置中严格评估模型性能.
主要成果:
- 使用EfficientNetV2-B1和EfficientNetV2-B2模型实现了98.09%的平均精度.
- 证明了CNN架构用于分类天空图像的高效性.
- 确定了在天空条件分类中产生最佳性能的特定EfficientNet模型.
结论:
- 这项研究证实了深度学习的巨大潜力,特别是CNN和TL,用于准确的天空图像分类.
- EfficientNetV2-B1和EfficientNetV2-B2模型对于实时评估天空状况非常有效,改善可再生能源预测.
- 准确的天空图像分类可以增强可再生能源系统运营中的决策,优化能源生产和消费.
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