美国国防部推进:太阳辐射建模的时间和分布优化的深度推进框架
1Osmaniye Vocational School, Electric & Energy Department, Osmaniye Korkut Ata University, Osmaniye, 80010, Turkey. ilkermert@osmaniye.edu.tr.
Scientific reports
|September 30, 2025
概括
本研究介绍了用于清洁能源系统的混合太阳辐射模型. 韦布尔 (WOA) - LSTM - XGBoost模型在预测太阳辐射时间模式方面表现出卓越的准确性.
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
- 统计建模 统计建模
背景情况:
- 准确的太阳辐射预测对于设计高效的清洁能源系统至关重要.
- 传统的方法经常与固有的随机性和太阳数据的时间变化性作斗争.
- 深度学习和统计分布拟合为改进太阳辐射建模提供了有希望的途径.
研究的目的:
- 开发和评估用于清洁能源系统设计的混合太阳辐射时间建模方法.
- 将统计分布匹配与深度学习技术集成在一起,以提高预测准确度.
- 为基于光伏的能源规划建立一个可转移的框架,特别是在发展中国家.
主要方法:
- 使用概率分布和参数优化通过最大概率估计 (MLE),鱼优化算法 (WOA) 和粒子群优化 (PSO) 来分析太阳辐射数据.
- 使用累积分布函数 (CDF) 与长期短期记忆网络 (LSTM),门式循环单位 (GRU) 和极端梯度增强 (XGBoost) 的混合时间建模的开发.
- 使用詹森-香农分歧 (JSD) 评估分布精度的模型的评估.
主要成果:
- 拟议的DOD-Boost框架,集成数据预处理,优化和时间建模,实现了高度准确的太阳辐射预测.
- 威布尔 (WOA) - LSTM - XGBoost混合动力车型表现最好,产生最低的JSD值为0.0084.
- 詹森-香农分歧 (JSD) 证明在评估预测和实际太阳辐射数据分布之间的相似性方面是有效的.
结论:
- 结合统计分布和深度学习的混合模型显著提高了太阳辐射时间建模的准确性.
- 开发的DOD-Boost框架为光伏能源规划提供了强大的和可转移的解决方案.
- 这些发现支持部署先进的可再生能源系统,特别是在数据基础设施有限的地区.
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