基于生物学的智能多目标优化,用于自动导出在南极气候条件下用于光伏电池板的可解释规则集
Erhan Arslan1, Ebru Akpinar2, Mehmet Das2
1Scientific and Technological Research Council of Türkiye, Marmara Research Center, Polar Research Institute, Gebze 41470, Türkiye.
这项研究开发了一种新的基于规则的模型,用于南极洲的光伏 (PV) 电力系统. 该模型为极端极地条件提供可靠的低碳能源解决方案,实现高精度和回忆.
科学领域:
- 可再生能源系统可再生能源系统
- 环境科学 环境科学
- 机器学习应用 机器学习应用
背景情况:
- 南极研究站需要可靠的低碳电源.
- 极端极地条件对能源发电构成重大挑战.
研究的目的:
- 为南极洲的光伏 (PV) 电力系统开发和验证一个可解释的,多目标的框架.
- 创建一个对极地条件的同步的PV气象数据集.
- 为了比较不同类型的光伏模块的性能.
主要方法:
- 在马岛编制了一个高分辨率 (30秒,1分钟,5分钟) 的光伏气象时间序列数据集.
- 开发了一个修改后的SPEA-2算法,以优化规则提取的精度和回忆.
- 基于规则的模型与基准机器学习模型 (kNN,SVM) 的比较.
- 使用精度,回忆,F1得分,平衡精度和MCC评估性能.
主要成果:
- 提出的基于规则的方法实现了具有竞争力的预测性能,可解释性和稳定性.
- 可解释人工智能 (XAI) 模型显示精度为92.3%,回忆率为89.7%.
- 在不同的光伏面板类型和采样间隔中,性能保持稳健.
结论:
- 该研究为 PV 系统在恶劣的高度环境中提供了一种新的,可解释的 AI 方法.
- 这些发现支持在极地研究站设计和运行可靠的光伏系统.
- 为南极太阳能研究创建了一个有价值的高分辨率数据集.
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