基于非参数定量回归的模拟,在南非对太阳辐射的添加效应
Amon Masache1, Daniel Maposa2, Precious Mdlongwa1
1Department of Statistics and Operations Research, National University of Science and Technology, Ascot, P.O. Box AC 939, Bulawayo, Zimbabwe.
准确的太阳辐射预测使用非参数定量回归 (QR) 来建模附加效应. 20%的预测时间和1万个数据点是改善可再生能源管理的理想条件.
科学领域:
- 可再生能源管理 管理可再生能源
- 统计建模 统计建模
- 时间序列分析时间序列分析
背景情况:
- 准确的太阳辐射预测对于有效的可再生能源管理至关重要.
- 添加效应显著影响太阳辐射预测的准确性.
- 非参数定量回归 (QR) 提供了一个强大的框架来建模这些效应.
研究的目的:
- 在太阳辐射中模拟添加效应,使用非参数的QR.
- 为了比较量子式通用增量模型 (QGAM),部分线性增量量子式回归 (PLAQR) 和增量量子式回归 (AQR) 的预测性能.
- 为了确定添加式太阳辐射建模的最佳预测地平线和样本大小.
主要方法:
- 在QR.中使用量子线来进行非参数组件近似.
- 纳入了部分线性添加量量子回归 (PLAQR) 模型,以考虑潜在的线性添加效应.
- 使用样本外指标,概率指标,密度图,墨菲图和Diebold-Mariano (DM) 测试来评估预测性能.
主要成果:
- 与PLAQR和AQR相比,定量通用添加模型 (QGAM) 在密度图,墨菲图和大多数指标得分方面表现略高.
- 虽然DM测试表明QGAM的准确性更高,但对PLAQR和AQR的彻底优越性尚未得到最终结论.
- 预测时间和样本大小显著影响了模型性能,在模型和指标之间存在差异.
结论:
- 建议使用非参数式QR进行增量太阳辐射效应建模时,预测地平线为20%,最低样本大小为10,000个数据点.
- 模型性能依赖于指标,建议为特定的概率预测需求量身定制的模型选择.
- 位置没有显著影响模型性能,但预测地平线和样本大小是关键因素.
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