Although the integration of solar photovoltaic (PV) systems is gaining widespread acceptance, the intermittency and instability of PV power generation lead to several operational challenges. PV power generation can be impacted by multiple environmental factors, such as the soiling of solar PV panels. There are some conventional methods proposed to deterministically estimate the solar power loss caused by soiling. However, the error of deterministic estimation cannot be eliminated due to the inherent volatility of solar power. Therefore, this paper proposes a probabilistic quantification method, namely SolarQRNN, to estimate the power loss by leveraging images captured by surveillance cameras. Specifically, the proposed model employs a novel quantile loss function and deep learning structures (backbone networks based on residual convolution units), which combines quantile regression and computer vision models for the first time. The proposed method has been extensively tested on a solar panel soiling image dataset. Test results indicate that SolarQRNN outperforms benchmark classification models with at least 51% improvements in evaluating metrics.

中文翻译：

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基于深度学习的太阳能光伏污染损失概率估计


尽管太阳能光伏（PV）系统的集成正在获得广泛接受，但光伏发电的间歇性和不稳定性导致了一些运营挑战。光伏发电可能会受到多种环境因素的影响，例如太阳能光伏板的污染。提出了一些传统方法来确定性地估计由污染引起的太阳能损失。然而，由于太阳能发电固有的波动性，确定性估计的误差无法消除。因此，本文提出了一种概率量化方法，即 SolarQRNN，利用监控摄像头捕获的图像来估计功率损耗。具体来说，所提出的模型采用了新颖的分位数损失函数和深度学习结构（基于残差卷积单元的骨干网络），首次将分位数回归和计算机视觉模型结合起来。所提出的方法已在太阳能电池板污染图像数据集上进行了广泛的测试。测试结果表明，SolarQRNN 的性能优于基准分类模型，在评估指标方面至少提高了 51%。