Observation-based statistical models have been widely used in forecasting solar energy; however, existing models often lack a clear relation to physics and are limited largely to global horizontal irradiance (GHI) forecasts over relatively short time horizons (<1 h). Incorporating physics into observation-based models, increasing forecast time horizons and developing a model system for forecasting not only GHI but also direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) remain challenging, especially under cloudy conditions because of complex cloud-radiation interactions. This work attempts to address these challenges by developing a hierarchy of four new physics-informed persistence models that can be used to simultaneously forecast GHI, DNI and DHI. The decade-long measurements (1998 to 2014) at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM)’s Southern Great Plains (SGP) Central Facility site are used to evaluate the performance of the new models. The results show that the new physics-informed forecast models generally outperform the simple and smart persistence models, and improve the forecast accuracy at lead times from 1.25 h up to 6 h. Further analysis reveals that the forecast error is highly related to the error and temporal variability of the assumed cloud predictor. The best model for forecasting different radiative components can be explained by the relationship between solar irradiances and cloud properties.

基于观测的统计模型已广泛用于预测太阳能。然而，现有的模型通常与物理学缺乏明确的联系，并且在相当短的时间范围内（<1 h）主要限于全球水平辐照度（GHI）预测。将物理学纳入基于观测的模型中，增加预测时间范围并开发模型系统来预测GHI以及直接法向辐照度（DNI）和水平漫射辐照度（DHI）仍然具有挑战性，尤其是在多云条件下，由于复杂的云辐射互动。这项工作试图通过建立四个新的基于物理学的持久性模型的层次结构来解决这些挑战，这些模型可用于同时预测GHI，DNI和DHI。美国长达十年的测量（1998年至2014年）能源部的大气辐射测量（ARM）的南部大平原（SGP）中央设施站点用于评估新模型的性能。结果表明，新的基于物理的信息预测模型通常优于简单的智能持久性模型，并且将交货时间从1.25 h提升到6 h可以提高预测准确性。进一步的分析表明，预测误差与假定的云预测器的误差和时间变化高度相关。可以通过太阳辐照度和云特性之间的关系来解释预测不同辐射分量的最佳模型。结果表明，新的基于物理的信息预测模型通常优于简单的智能持久性模型，并且将交货时间从1.25 h提升到6 h可以提高预测准确性。进一步的分析表明，预测误差与假定的云预测器的误差和时间变化高度相关。可以通过太阳辐照度和云特性之间的关系来解释预测不同辐射分量的最佳模型。结果表明，新的基于物理的信息预测模型通常优于简单的智能持久性模型，并且将交货时间从1.25 h提升到6 h可以提高预测准确性。进一步的分析表明，预测误差与假定的云预测器的误差和时间变化高度相关。可以通过太阳辐照度和云特性之间的关系来解释预测不同辐射分量的最佳模型。