Reflectance losses on solar mirrors due to soiling pose a formidable challenge for Concentrating Solar Power (CSP) plants. Soiling can vary significantly from site to site -- from fractions of a percent to several percentage points per day (pp/day), a fact that has motivated several studies in soiling predictive modelling. Yet, existing studies have so far neglected the characterization of statistical uncertainty in their parameters and predictions. In this paper, two reflectance loss models are proposed that model uncertainty: an extension of a previously developed physical model and a simplified model. A novel uncertainty characterization enables Maximum Likelihood Estimation techniques for parameter estimation for both models, and permits the estimation of parameter (and prediction) confidence intervals. The models are applied to data from ten soiling campaigns conducted at three Australian sites (Brisbane, Mount Isa, Wodonga). The simplified model produces high-quality predictions of soiling losses on novel data, while the semi-physical model performance is mixed. The statistical distributions of daily losses were estimated for different dust loadings. Under median conditions, the daily soiling losses for Brisbane, Mount Isa, and Wodonga are estimated as $0.53 \pm 0.62$, $0.1 \pm 0.1$, and $0.57 \pm 0.14$ pp/day, respectively. Yet, higher observed dust loadings can drive average losses as high as $2.50$ pp/day. Overall, the results suggest a relatively simple approach characterizing the statistical distributions of soiling losses using airborne dust measurements and short reflectance monitoring campaigns.

翻译：太阳镜因污损导致的反射率损失对聚光太阳能电站构成严峻挑战。不同场址的污损程度差异显著——从每日百分之零点几到数个百分点的损失（pp/天），这一事实推动了多项关于污损预测建模的研究。然而，现有研究至今未能对其参数和预测中的统计不确定性进行刻画。本文提出两种能表征不确定性的反射率损失模型：一种是对先前物理模型的扩展，另一种是简化模型。新颖的不确定性表征方法使两种模型均能采用最大似然估计技术进行参数估计，并可估算参数（及预测）置信区间。模型基于三个澳大利亚场址（布里斯班、伊萨山、沃东加）开展的十次污损监测数据进行了验证。简化模型能高质量预测新数据的污损损失，而半物理模型性能表现参差不齐。针对不同粉尘负荷，我们估算了日损失值的统计分布。在中等条件下，布里斯班、伊萨山和沃东加的日污损损失分别估计为$0.53 \pm 0.62$、$0.1 \pm 0.1$和$0.57 \pm 0.14$ pp/天。然而，观测到的高粉尘负荷可使平均损失高达$2.50$ pp/天。总体而言，结果表明：利用气载粉尘测量数据和短期反射率监测活动即可通过相对简化的方法表征污损损失的统计分布。