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Probabilistic solar irradiance transposition models
Renewable and Sustainable Energy Reviews ( IF 15.9 ) Pub Date : 2020-03-11 , DOI: 10.1016/j.rser.2020.109814
Hao Quan , Dazhi Yang

Transposition models convert the solar irradiance received on a horizontal surface to in-plane irradiance. All transposition models to date, unfortunately, only produce deterministic (as oppose to probabilistic) estimates. In modern energy meteorology, having the entire predictive distribution is more desirable than relying only on deterministic estimates. To that end, this paper outlines two strategies for creating probabilistic transposition models (PTMs), that can quantify the various types of uncertainty involved in the modeling process. The first strategy seeks the analytic expressions of measurement, model, and parameter uncertainty, and the final predictive variance is the sum of these three types of uncertainty. On the other hand, the second strategy directly models the overall uncertainty as a whole, and uses ensemble model output statistics to estimate the predictive distribution through optimizing a loss function. Both strategies generate estimates of tilted irradiance with Gaussian predictive distributions. As compared to their deterministic counterparts, PTMs clearly offer more insights on uncertainty quantification, during solar energy system design, simulation, performance evaluation, and power output forecasting.



中文翻译:

概率太阳辐照度转换模型

换位模型将在水平表面上接收到的太阳辐照度转换为平面内辐照度。不幸的是,迄今为止,所有的转置模型都只能产生确定性的估计(与概率相反)。在现代能源气象学中,拥有整个预测分布比仅依靠确定性估计更为可取。为此,本文概述了两种创建概率转换模型(PTM)的策略,这些策略可以量化建模过程中涉及的各种不确定性。第一种策略寻求测量,模型和参数不确定性的解析表达式,最终的预测方差是这三种不确定性的总和。另一方面,第二种策略直接对整体不确定性进行建模,并使用集成模型输出统计信息通过优化损失函数来估算预测分布。两种策略都可以利用高斯预测分布来生成倾斜辐照度的估计值。与确定性同类产品相比,PTM在太阳能系统设计,仿真,性能评估和功率输出预测期间,显然提供了更多关于不确定性量化的见解。

更新日期:2020-03-12
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