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在太阳能预测中对深度学习架构进行比较分析
Montaser Abdelsattar1, Mohamed A Azim2, Ahmed AbdelMoety3
1Electrical Engineering Department, Faculty of Engineering, South Valley University, Qena, 83523, Egypt. Montaser.A.Elsattar@eng.svu.edu.eg.
Scientific reports
|August 28, 2025
概括
深度学习模型可以提高智能电网的太阳能预测. 时间卷积网络 (TCN) 和自动编码模型显示了精确太阳能预测的最佳性能.
科学领域:
- 可再生能源系统
- 在能源领域的人工智能
- 时间序列预测
背景情况:
- 准确的太阳能预测对于将可再生能源纳入电网至关重要.
- 现有的预测方法在可靠性和准确性方面面临挑战,特别是复杂的时间模式.
- 深度学习 (DL) 为改善太阳能预测提供了潜在的解决方案.
研究的目的:
- 进行对8个最先进的深度学习架构进行比较分析.
- 使用关键统计指标 (RMSE,MAE,MAPE,R2) 评估DL模型的性能.
- 确定最有效的DL模型,并为现实世界能源系统提供框架.
主要方法:
- 使用了4200个历史太阳能记录以及20个气象和天文特征的数据集.
- 将八个DL架构进行比较:自动编码器,LSTM,GRU,SimpleRNN,CNN,TCN,变压器和InformerLite.
- 使用RMSE,MAE,MAPE和R2指标对训练,验证和测试数据集的模型性能进行评估.
主要成果:
- 时间卷积网络 (TCN) 显示出优异的性能,测试R2为0. 7786和平衡相对标准偏差为0. 6827.
- 自动编码器模型在整个数据集中表现出最高的整体性能,整体R2为0.8437.
- 变压器模型的性能明显较差 (试验R2=0.0714),表明没有修改的限制.
结论:
- 基于统计指标的太阳能预测中,TCN和自动编码器被认为是最有效的深度学习模型.
- 该研究为智能电网应用提供了可扩展,可解释和可扩展的预测框架.
- 这些发现支持对DL进行明智的整合,以加强可再生能源管理和未来的混合建模.
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