太阳辐射的短期预测使用基于决策树的模型和非参数定量回归
Amon Masache1, Precious Mdlongwa1, Daniel Maposa2
1Department of Statistics and Operations Research, National University of Science and Technology, Bulawayo, Zimbabwe.
PloS one
|December 11, 2024
概括
准确的太阳辐射预测对于可再生能源至关重要. 在太阳辐射预测方面,量子通用添加模型 (QGAM) 优于量子回归随机森林 (QRRF),提供更高的准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 统计建模 统计建模
- 气象预报 气象预报
背景情况:
- 准确的太阳辐射 (SI) 预测对于管理可再生能源发电和供应至关重要.
- 太阳能发电的间歇性需要先进的预测技术.
- 现有的SI研究往往侧重于条件平均分布,限制不确定性表示.
研究的目的:
- 评估和比较阳光辐射的量子回归随机森林 (QRRF) 和量子泛化添加模型 (QGAM) 的预测性能.
- 引入QRRF和QGAM作为SI研究的新型预测框架.
- 评估模型在预测间隔之外表示预测不确定性的能力.
主要方法:
- 使用多变量数据生成过程进行模拟研究,以比较预测准确度.
- 模型的评估使用球损失得分和平均绝对缩放误差.
- 模型应用于现实生活中的太阳辐射数据.
主要成果:
- 在预测预测分布方面,QRRF表现与QGAM相当.
- 在球损失和平均绝对缩放误差方面,QGAM明显优于QRRF.
- QRRF和QGAM都通过量子估计提供了关于预测不确定性的完整信息.
结论:
- 由于其卓越的准确性,QGAM建议使用基于决策树的模型,如QRRF用于太阳辐射预测.
- QRRF可以作为预测预测分布的替代方案.
- QRRF和QGAM框架可以扩展到模拟其他依赖气象的可再生能源.
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