Evaluation of statistical learning configurations for gridded solar irradiance forecasting
Evaluation of statistical learning configurations for gridded solar irradiance forecasting
复制标题
网格太阳辐照度预测统计学习配置的评估
DOI：
10.1016/j.solener.2017.04.031
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发表时间：
2017
期刊：
影响因子：
6.7
通讯作者：
John K. Williams
中科院分区：
文献类型：
--
作者：
D. Gagne;A. McGovern;S. E. Haupt;John K. Williams
越来越需要对太阳辐照度进行网格化预测，以便将分布式太阳能设施和没有长期观测辐照度记录的较新大规模设施的电力整合到电网中。我们评估统计学习模型的不同组合和来自观测站点的天气数据的聚合，以确定哪种组合在独立站点产生最低的预测误差。评估揭示了统计学习模型的选择、与训练数据的贴近度、训练数据聚合和内插方法如何影响未包括在训练数据中的俄克拉荷马州中间网点的清晰度指数的预测。结果表明，统计学习模型、内插方案和损失函数的选择对性能的影响最大。在天气晴朗和距离训练地点较近的考点，错误往往较低。所有的统计学习方法和NWP模式输出都产生了可靠的预测，但与观测值相比，低估了云量的频率。
Gridded forecasts of solar irradiance are increasingly needed to integrate power into the electric grid from distributed solar installations and newer large-scale installations that don’t have long records of observed irradiance. We evaluate different combinations of statistical learning models and aggregations of weather data from observed sites to identify which combination produces the lowest forecast errors at independent sites. The evaluation reveals how statistical learning model choice, closeness of fit to training data, training data aggregation, and interpolation method affect forecasts of clearness index at Oklahoma Mesonet sites not included in the training data. It shows that the choices of statistical learning model, interpolation scheme, and loss function have the biggest impacts on performance. Errors tend to be lower at testing sites with sunnier weather and those that are closer to training sites. All of the statistical learning methods and the NWP model output produce reliable predictions but underestimate the frequency of cloudiness compared to observations.