论文标题
太阳辐照度预测的云红外图像中的多层风速场可视化
Multi-Layer Wind Velocity Field Visualization in Infrared Images of Clouds for Solar Irradiance Forecasting
论文作者
论文摘要
由于生成时天气条件，太阳能动力网格中可用的能量尚不确定。预测全球太阳能辐照度可以通过安排存储和派遣能源的能力来解决此问题。云对太阳的阻塞是太阳能产生的不稳定性的主要原因。这项研究提出了一种方法，可以在图像中有多个风速场时以云的长波红外图像的序列可视化风速场。该方法可用于预测云通过云的阻塞，从而在太阳能的产生中稳定。实施无监督的学习是为了推断红外图像中多个风速场中云速度向量和高度的分布。具有流量约束的多输出加权支持向量机可将风速度字段推送到整个帧，可视化云的路径。所提出的方法能够使用从红外图像中提取的云的速度向量和物理特征近似于小型空气包裹中的风速场。假设流线是途径，则可以将风速度场的可视化用于预测太阳的云。当考虑提高太阳能产生的稳定性的方式时，这很重要。
The energy available in a solar energy powered grid is uncertain due to the weather conditions at the time of generation. Forecasting global solar irradiance could address this problem by providing the power grid with the capability of scheduling the storage and dispatch of energy. The occlusion of the Sun by clouds is the main cause of instabilities in the generation of solar energy. This investigation proposes a method to visualize the wind velocity field in sequences of longwave infrared images of clouds when there are multiple wind velocity fields in an image. This method can be used to forecast the occlusion of the Sun by clouds, providing stability in the generation of solar energy. Unsupervised learning is implemented to infer the distribution of the clouds' velocity vectors and heights in multiple wind velocity fields in an infrared image. A multi-output weighted support vector machine with flow constraints is used to extrapolate the wind velocity fields to the entire frame, visualizing the path of the clouds. The proposed method is capable of approximating the wind velocity field in a small air parcel using the velocity vectors and physical features of clouds extracted from infrared images. Assuming that the streamlines are pathlines, the visualization of the wind velocity field can be used for forecasting cloud occlusions of the Sun. This is of importance when considering ways of increasing the stability of solar energy generation.
