Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods
Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods
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DOI：
10.3390/atmos12030395
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发表时间：
2021-03-01
期刊：
影响因子：
2.9
通讯作者：
Beckman, Pete H.
中科院分区：
文献类型：
--
作者：
Park, Seongha;Kim, Yongho;Beckman, Pete H.
从面向天空的相机拍摄的图像中估计云量可以成为分析当前天气状况和估计光伏发电量的重要输入。然而，云的位置、形状和密度的不断变化使得开发用于云覆盖估计的鲁棒计算方法具有挑战性。准确地确定云的边缘以及云和晴朗天空之间的分离是困难的，通常是不可能的。为了确定用于估计光伏输出的云量，我们建议使用机器学习方法进行云分割。我们比较了几种方法，包括经典的回归模型，深度学习方法和结合其他机器学习模型结果的联合收割机的提升方法。为了用各种天空条件训练每个机器学习模型，我们用配备摄像头的Waggle传感器节点收集的朦胧和阴天图像补充了现有的新加坡全天空成像分割数据库。我们发现，U-Net架构（我们使用的深度神经网络之一）最准确地分割了云像素。然而，云像元分割的准确性并不能保证太阳辐照度估计的高精度。我们证实了云覆盖率与太阳辐照度直接相关。此外，我们证实了太阳辐照度和太阳能发电量密切相关;因此，通过预测太阳辐照度，我们可以估计太阳能发电量。这项研究表明，采用机器学习方法的面向天空的摄像机可以用来估计太阳能输出。这种基于地面的方法提供了一种廉价的方式来了解太阳辐照度和估计光伏太阳能设施的产量。
Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities.