Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.

中文翻译：

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光伏组件背面温度的瞬态加权移动平均模型

光伏 (PV) 性能的准确建模需要精确计算模块温度。目前，大多数温度模型依赖于不考虑瞬态气候条件和模块热质量的稳态假设。另一方面，基于物理的复杂瞬态模型计算量大且难以参数化。为了解决这个问题，开发了一种新的瞬态热建模方法，其中对先前时间步长的稳态预测进行加权和平均，以在更精细的时间尺度上准确预测模块温度。该模型采用 3-D 有限元分析，用于计算风速和模块单位质量对模块温度的影响。该模型在应用中，作为现有稳态模型的附加过滤器，可以消除由于太阳能资源的间歇性导致的错误值。该移动平均模型的验证表明，与基于平均绝对误差改进的稳态模型相比，它可以将整体 PV 能源性能模型精度提高多达 0.58%，并且可以显着降低模型预测和测量温度之间的变异性时间序列数据。