We conduct the first comprehensive meta-analysis of deterministic solar forecasting based on skill score, screening 1,447 papers from Google Scholar and reviewing the full texts of 320 papers for data extraction. A database of 4,758 points was built and analyzed with multivariate adaptive regression spline modelling, partial dependence plots, and linear regression. Notably, the analysis accounts for the most important non-linear relationships and interaction terms in the data. We quantify the impacts on forecast accuracy of important variables such as forecast horizon, resolution, climate conditions, regions' annual solar irradiance level, power system size and capacity, forecast models, train and test sets, and the use of different techniques and inputs. By controlling for the key differences between forecasts, including location variables, the findings from the analysis can be applied globally. An overview of scientific progress in the field is also provided.

翻译：我们根据技能评分,对确定性太阳预报进行第一次综合元分析,筛选谷歌学者的1 447份文件,并审查320份文件的全文,以数据提取为目的,建立并分析了4 758个点的数据库,采用多变的适应性回归样板模型、部分依赖性地块和线性回归法,值得注意的是,分析说明了数据中最重要的非线性关系和互动术语,我们量化了预测地平线、分辨率、气候条件、区域年度太阳辐照水平、电力系统规模和能力、预测模型、培训和测试成套材料以及使用不同技术和投入等重要变量对预测准确性的影响,通过控制预测之间的关键差异,包括地点变量,分析结果可以在全球范围内应用,还提供了该领域科学进展概览。