Photovoltaics (PV), the primary use of solar energy, is growing rapidly. However, the variable output of PV under changing weather conditions may hinder the large-scale deployment of PV. In this study, we propose a two-stage classification-prediction framework to predict contemporaneous PV power output from sky images (a so-called “nowcast”), and compare it with an end-to-end convolution neural network (CNN). The proposed framework first classifies input images into different sky conditions and then the classified images are sent to specific sub-models for PV output prediction. Two types of classifiers are developed and compared: (1) a CNN-based classifier trained on clear sky index (CSI)-labeled sky images and (2) a physics-based non-parametric classifier based on a threshold of fractional cloudiness of sky images. Different numbers of classification categories are also examined. The results suggest that the cloudiness-based classifier is more suitable than the CSI-based classifier for the framework, and the 3-class classification (i.e., sunny, cloudy, overcast) is found to be the optimal choice. We then fine-tune the cloudiness threshold for the non-parametric classifier and tailor the architecture for each sky-condition-specific sub-model. Under the best design, the proposed framework can achieve a root mean squared error (RMSE) of 2.20 kW (relative to a 30 kW rated PV array) on the test set comprising 18 complete days (9 sunny, RMSE = 0.69 kW; 9 cloudy, RMSE = 3.06 kW). Compared with the end-to-end CNN baseline model, the overall prediction performance can be improved by 6% (7% in sunny and 6% in cloudy), with 6% fewer trainable parameters needed in the architecture.

中文翻译：

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使用卷积神经网络从天空图像预测光伏功率输出：天空条件特定子模型和端到端模型的比较

光伏 (PV) 是太阳能的主要用途，正在迅速增长。然而，光伏在不断变化的天气条件下的可变输出可能会阻碍光伏的大规模部署。在这项研究中，我们提出了一个两阶段分类预测框架来预测来自天空图像（所谓的“临近预报”）的同期光伏功率输出，并将其与端到端卷积神经网络 (CNN) 进行比较。所提出的框架首先将输入图像分类为不同的天空条件，然后将分类的图像发送到特定的子模型进行 PV 输出预测。开发和比较了两种类型的分类器：（1）基于晴空指数（CSI）标记的天空图像训练的基于 CNN 的分类器和（2）基于天空分数云度阈值的基于物理的非参数分类器图片。还检查了不同数量的分类类别。结果表明，基于云度的分类器比基于 CSI 的分类器更适合该框架，并且发现 3 级分类（即晴天、多云、阴天）是最佳选择。然后，我们微调非参数分类器的云度阈值，并为每个特定于天空条件的子模型定制架构。在最佳设计下，所提出的框架可以在包含 18 个完整天数（9 个晴天，RMSE = 0.69 kW；9 个阴天）的测试集上实现 2.20 kW（相对于 30 kW 额定光伏阵列）的均方根误差（RMSE） ，RMSE = 3.06 千瓦）。与端到端的CNN基线模型相比，整体预测性能可以提高6%（晴天7%，阴天6%），