一种机器学习方法来评估气候变化对单轴和双轴跟踪光伏系统的影响
Udit Mamodiya1, Indra Kishor2, Priyam Ganguly3
1Faculty of Engineering and Technology, Poornima University, Jaipur, 303905, Rajasthan, India.
Scientific reports
|July 10, 2025
概括
气候优化机器学习自适应跟踪 (COMLAT) 通过使用人工智能进行实时调整来提高太阳能输出. 与传统方法相比,这种自适应式太阳能追踪系统显著提高了能源生产.
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
背景情况:
- 太阳能跟踪系统的效率受到气候变化的阻碍.
- 传统的跟踪方法 (固定倾斜,单轴,双轴) 缺乏实时适应性,导致能量损失.
研究的目的:
- 介绍COMLAT (气候优化机器学习自适应跟踪),一个人工智能驱动的太阳能跟踪系统.
- 通过实时自适应跟踪机制优化太阳能输出.
主要方法:
- 使用CNN-LSTM进行气候预测,使用XGBoost进行能源产量估计.
- 采用深度Q学习 (DQL) 进行实时跟踪控制.
- 在印度贾普尔进行了一年的实验研究,将COMLAT与传统系统进行比较.
主要成果:
- COMLAT显示,固定倾斜系统的能源生产增加了55%,双轴系统的能源生产增加了15-20%.
- 实现了10天的太阳辐射预测,RMSE为23.5W/m2,表现优于LSTM和GRU.
- XGBoost模型在能源输出预测方面获得了0.94的R2得分.
结论:
- COMLAT为智能电网和太阳能发电场提供了一个可扩展和工业可行的解决方案.
- 基于AI的自适应跟踪实时优化角度,最大限度地减少机械运动,最大限度地提高效率.
- 未来的研究将集中在计算效率,储能和自我学习算法上.
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