论文标题
基于时间一致的基于图像的太阳跟踪算法，用于太阳能预测应用
A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications
论文作者
论文摘要
改善辐照度预测对于进一步增加太阳能在能量组合中的份额至关重要。在短时间内，地面上的鱼眼摄像机用于捕获云位移，导致电力生产的局部变异性。由于大多数太阳辐射直接来自太阳，因此当前的预测方法将其在图像中的位置作为解释云覆盖动力学的参考。但是，现有的太阳跟踪方法依靠外部数据和相机的校准，这需要访问设备。为了解决这些局限性，本研究引入了一种基于图像的太阳跟踪算法，以在可见时将太阳定位在图像中，并从过去的观察中插入其日常轨迹。我们在SIRTA的实验室一年收集的一组天空图像上验证了该方法。实验结果表明，所提出的方法提供了强大的光滑太阳轨迹，平均绝对误差低于图像大小的1％。
Improving irradiance forecasting is critical to further increase the share of solar in the energy mix. On a short time scale, fish-eye cameras on the ground are used to capture cloud displacements causing the local variability of the electricity production. As most of the solar radiation comes directly from the Sun, current forecasting approaches use its position in the image as a reference to interpret the cloud cover dynamics. However, existing Sun tracking methods rely on external data and a calibration of the camera, which requires access to the device. To address these limitations, this study introduces an image-based Sun tracking algorithm to localise the Sun in the image when it is visible and interpolate its daily trajectory from past observations. We validate the method on a set of sky images collected over a year at SIRTA's lab. Experimental results show that the proposed method provides robust smooth Sun trajectories with a mean absolute error below 1% of the image size.
