论文标题
北欧栅格太阳辐射产品的机器学习适应现场适应
Site adaptation with machine learning for a Northern Europe gridded solar radiation product
论文作者
论文摘要
网格全球水平辐照度（GHI）数据库是分析太阳能应用技术和经济方面的基础，尤其是光伏应用。如今，存在许多网格的GHI数据库，其质量已通过基于地面的辐照度测量得到了彻底验证。 Nonetheless, databases that generate data at latitudes above 65$^{\circ}$ are few, and those available gridded irradiance products, which are either reanalysis or based on polar orbiters, such as ERA5, COSMO-REA6, or CM SAF CLARA-A2, generally have lower quality or a coarser time resolution than those gridded irradiance products based on geostationary satellites.在高纬度网格GHI数据库中，由瑞典气象和水文研究所（SMHI）开发的Strång模型可能是最准确的模型，它在瑞典提供了数据。为了进一步提高产品质量，此处用于改善Strång数据集的校准技术，该技术旨在根据短期的高质量辐照度测量值来调整长期的低质量网格辐照度估计。这项研究与常规统计方法不同，采用了机器学习以进行现场适应。已经分析了九种机器学习算法并与常规统计算法进行比较，以识别瑞典最有利的网站适应技术。 SMHI的三个气象站用于培训和验证。结果表明，由于模型性能中的时空异质性，无法识别通用模型，这表明该位点适应性是一个与位置有关的过程。
Gridded global horizontal irradiance (GHI) databases are fundamental for analysing solar energy applications' technical and economic aspects, particularly photovoltaic applications. Today, there exist numerous gridded GHI databases whose quality has been thoroughly validated against ground-based irradiance measurements. Nonetheless, databases that generate data at latitudes above 65$^{\circ}$ are few, and those available gridded irradiance products, which are either reanalysis or based on polar orbiters, such as ERA5, COSMO-REA6, or CM SAF CLARA-A2, generally have lower quality or a coarser time resolution than those gridded irradiance products based on geostationary satellites. Among the high-latitude gridded GHI databases, the STRÅNG model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is likely the most accurate one, providing data across Sweden. To further enhance the product quality, the calibration technique called "site adaptation" is herein used to improve the STRÅNG dataset, which seeks to adjust a long period of low-quality gridded irradiance estimates based on a short period of high-quality irradiance measurements. This study, differing from the conventional statistical approaches, adopts machine learning for site adaptation. Nine machine-learning algorithms have been analysed and compared with conventional statistical ones to identify Sweden's most favourable technique for site adaptation. Three weather stations of SMHI are used for training and validation. The results show that, due to the spatio-temporal heterogeneity in model performance, no universal model can be identified, which suggests that site adaptation is a location-dependent procedure.
