Improving prediction of surface solar irradiance variability by integrating observed cloud characteristics and machine learning
Improving prediction of surface solar irradiance variability by integrating observed cloud characteristics and machine learning
复制标题
通过整合观测到的云特征和机器学习来改进对表面太阳辐照度变化的预测
DOI：
10.1016/j.solener.2021.07.047
复制
发表时间：
2021
期刊：
影响因子：
6.7
通讯作者：
L. Berg
中科院分区：
文献类型：
--
作者：
L. Riihimaki;Xinya Li;Z. Hou;L. Berg
制作了一个5年1分钟分辨率的云和太阳辐射观测数据集，其中包括由于云类型和部分天空覆盖而引起的表面太阳辐照度变化的两个度量。训练多元回归模型以拟合来自这两个云属性预报器的地面太阳辐照度变化的观测结果。我们发现，基于集成树的方法，随机森林和梯度提升机，具有最少的过拟合问题，并显示出最佳性能，R2为0.42。虽然本研究中训练的观测数据仅来自一个站点，即位于俄克拉荷马州的美国能源部（DOE）大气辐射测量（ARM）南部大平原（SGP）站点，但统计数据季节性的初步比较表明，这些结果相对独立于天气状况;将在未来的工作中测试跨站点发现的一般性。观测数据和开发的机器学习模型正在用于创建数值天气预测模型参数化，以便以计算效率高的方式进行前一天的太阳变化预测。这是朝着创建一个新的预测未来一天变化的范例迈出的第一步，有可能提供一个新的工具来改善电网运营，规划和弹性。
A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.
影响因子：
8
作者：
Jimenez, Pedro A.;Hacker, Joshua P.;Deng, Aijun
通讯作者：
Deng, Aijun