基于技能分数的太阳能预测元分析
A Meta-Analysis of Solar Forecasting Based on Skill Score
AI总结：
本文通过首个基于技能分数的太阳能预测元分析，量化了时间尺度、气候区、输入数据与模型类型等因素对预测精度的影响，发现集成-混合模型最准确、图像方法适合日内预测，并揭示了训练数据过多会导致过拟合。
摘要：
我们开展了首个基于技能分数的确定性太阳能预测综合元分析，从谷歌学术筛选了1,447篇论文，并审阅了其中320篇论文的全文以进行数据提取。构建了一个包含4,687个数据点的数据库，并使用多元自适应回归样条建模、部分依赖图和线性回归进行分析。量化了十个因素对技能分数的边际影响。分析显示了数据库中变量之间的非线性和复杂交互作用。预测时间尺度具有核心影响，并主导其他因素的影响。因此，太阳能预测的分析应针对每个时间尺度分别进行。气候区变量与技能分数具有统计显著的相关性。在输入方面，历史数据和时空信息非常有帮助。对于日内预测，天空图像和卫星图像显示出最高的重要性。对于日前预测，数值天气预报和本地实测气象数据非常有效。对所有预测模型进行了比较。集成-混合模型在所有时间尺度上均实现了最准确的预测。混合模型在小时内预测中表现出优越性，而基于图像的方法对于日内预测最为有效。更多的训练数据可以提升技能分数。然而，当训练数据过多（超过2000天）时，观察到过拟合现象。太阳能预测精度已有显著提升，尤其是近年来。小时内和日内预测的改进幅度大于日前预测。通过控制预测之间的关键差异，包括位置变量，我们的研究结果可应用于全球范围。
英文摘要：
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 4,687 points was built and analyzed with multivariate adaptive regression spline modelling, partial dependence plots, and linear regression. The marginal impacts on skill score of ten factors were quantified. The analysis shows the non-linearity and complex interaction between variables in the database. Forecast horizon has a central impact and dominates other factors' impacts. Therefore, the analysis of solar forecasts should be done separately for each horizon. Climate zone variables have statistically significant correlation with skill score. Regarding inputs, historical data and spatial temporal information are highly helpful. For intra-day, sky and satellite images show the most importance. For day-ahead, numerical weather predictions and locally measured meteorological data are very efficient. All forecast models were compared. Ensemble-hybrid models achieve the most accurate forecasts for all horizons. Hybrid models show superiority for intra-hour while image-based methods are the most efficient for intra-day forecasts. More training data can enhance skill score. However, over-fitting is observed when there is too much training data (longer than 2000 days). There has been a substantial improvement in solar forecast accuracy, especially in recent years. More improvement is observed for intra-hour and intra-day than day-ahead forecasts. By controlling for the key differences between forecasts, including location variables, our findings can be applied globally.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
