通过网络安全的深度联合学习实现全球太阳辐射预测模型的通用化
Arash Moradzadeh1, Hamed Moayyed2, Behnam Mohammadi-Ivatloo3,4
1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 5166616471, Iran.
Environmental science and pollution research international
|October 14, 2023
概括
联合学习 (FL) 和卷积神经网络 (CNN) 能够在全球范围内准确地预测太阳辐射. 这种方法保护了数据隐私,并在缺乏培训数据的新区域中表现出色.
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
- 气候建模气候模型
背景情况:
- 全球日益普及的太阳能需要精确的太阳辐射预测.
- 数据稀缺和隐私问题阻碍了传统的太阳能预测方法.
- 集中分析的分布式数据收集带来了重大挑战.
研究的目的:
- 开发一个保护隐私的全球太阳辐射预测模型.
- 利用联合学习 (FL) 和卷积神经网络 (CNN) 来提高预测准确性.
- 建立一个能够在数据稀缺地区进行预测的全球超级模型.
主要方法:
- 利用联合学习 (FL) 在多个客户端进行分散的模型培训.
- 采用卷积神经网络 (CNN) 来分析来自伊朗八个地区的各种气候数据.
- 测试了全球超级模特在伊朗三个新地区 (Abadeh,Jarqavieh,Arak) 的有效性,没有先前的培训数据.
主要成果:
- 全球超级模特FL-CNN在新地区实现了高精度:95% (阿巴德),92% (贾尔卡维耶) 和90% (阿拉克).
- 由于缺乏培训数据,传统的机器学习和深度学习模型无法预测这些新区域的太阳辐射.
- 拟议的FL方法与传统方法相比,表现优越,特别是在数据稀缺的环境中.
结论:
- 联合学习与CNN相结合,为全球太阳辐射预测提供了强大的解决方案.
- 开发的模型有效地解决了数据隐私问题,克服了数据可用性的局限性.
- 这种保护隐私,数据高效的方法显著推进了可再生能源预测领域.
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