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一个增强的CNN与ResNet50和LSTM深度学习预测模型用于气候变化决策.

Ahmed M Elshewey1, Mona M Jamjoom2, Eman H Alkhammash3

  • 1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O. BOX: 43221, Suez, Egypt. ahmed.elshewey@fci.suezuni.edu.eg.

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概括

准确的气候变化预测温度和风力发电对于风能系统至关重要. 一个新的混合深度学习模型,CNN-ResNet50-LSTM,在预测这些因素方面表现出卓越的表现,有助于未来的能源规划.

关键词:
在美国,CNN是CNN.气候变化 气候变化 气候变化深度学习是一种深度学习.这是LSTM的LSTM.在ResNet50中使用ResNet50温度 温度是指温度.风力发电是风力发电的重要组成部分.

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科学领域:

  • 环境科学与可再生能源
  • 人工智能和机器学习

背景情况:

  • 气候变化通过气温,风速和风速模式的变化对风能生产产生重大影响.
  • 准确预测气候因素对于稳定的风能系统运行和有效的电力管理至关重要.
  • 传统的预测模型与气候数据中的复杂,非线性关系作斗争,限制了它们的准确性.

研究的目的:

  • 开发和评估一种混合深度学习模型,以提高气候变化下的温度和风能预测.
  • 解决传统模型在捕获复杂气候数据动态方面的局限性.
  • 为改善风能系统规划和管理提供一个工具.

主要方法:

  • 开发了一个混合深度学习模型,CNN-ResNet50-LSTM,集成卷积神经网络 (CNN),ResNet50和长短期记忆 (LSTM).
  • 利用了三个公共数据集:风力轮机斯卡达 (Scada),沙特阿拉伯天气历史 (SA) 和风力发电数据 (WPG).
  • 使用R平方,MSE,MAE,MedAE和RMSE等指标评估预测准确度,与五种传统回归模型进行比较.

主要成果:

  • 在所有测试的数据集和预测任务中,CNN-ResNet50-LSTM模型实现了卓越的性能.
  • 获得的R平方得分为98.84% (风力发电,斯卡达),99.01% (温度,SA),98.58% (温度,WPG) 和98.35% (风力发电,WPG).
  • 在预测准确度方面,与传统的回归模型相比,显著改进.

结论:

  • CNN-ResNet50-LSTM混合模型对于预测温度和风力发电非常有效,这对于风能在气候变化适应方面至关重要.
  • 该模型显示了长期气候变化预测的巨大潜力,应用范围可达2030年.
  • 这种先进的预测能力支持更好的决策,并提高风能基础设施的弹性.