基于GAN的太阳辐射预测方法:数据增强和模型优化,用于沙特阿拉伯
Abdalla Alameen1, Sultan Mesfer Aldossary1
1Department of Computer Engineering and Information, Prince Sattam Bin Abdulaziz University, Wadi ad-Dawasir, Riyadh, Saudi Arabia.
PeerJ. Computer science
|September 24, 2025
概括
生成对抗网络 (GAN) 创建合成太阳辐射数据以改善可再生能源预测. 这种方法提高了模型的准确性和适应性,这对于优化太阳能发电系统至关重要.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的太阳辐射预测对于可再生能源优化至关重要,但由于数据稀缺性和可变性而受到阻碍.
- 生成对抗网络 (GAN) 用于生成高质量的合成太阳辐射数据,解决数据限制.
研究的目的:
- 开发一个新的框架,将GAN生成的合成数据与机器学习和深度学习模型集成在一起.
- 提高不同气候区的太阳辐射预测模型的准确性和适应性.
主要方法:
- 开发了一个框架,将GAN生成的合成数据与CNN-LSTM架构集成.
- 用增强数据集训练和评估模型,提高预测准确性和概括性.
主要成果:
- 在增强数据集上训练的模型显示出显著的改善:根平均平方误差 (RMSE) 减少了15.2%,平均绝对误差 (MAE) 减少了19.9%.
- 该框架有效地弥补了数据缺口,并加强了沙特阿拉伯各个气候地区的模型概括.
结论:
- 拟议的框架支持实用应用,如光伏系统优化和电网稳定性.
- 这种可扩展和适应的方法与沙特阿拉伯的2030年愿景和全球可再生能源目标相一致.
- 为推进可持续能源解决方案,建议对计算复杂性和超参数灵敏度进行进一步的研究.
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