SPLTRAK Abstract Submission
Cloud Segmentation and Motion Tracking in Sky Images
Benjamin G Pierce, Joshua S Stein, Jennifer L Braid, Daniel Riley
Sandia National Laboratories, Albuquerque, NM, United States

In this work, we present two different algorithms to aid in real-time weather predictions. This information can be used to inform the movement of a tracker or short-term power predictions. Since cloud cover significantly affects the resulting insolation on a PV module, identifying and tracking cloud motion is useful to this end. This work presents a convolutional autoencoder (CAE) to identify clouds and a particle tracker to predict cloud movement. The CAE model integrates information from multiple approaches to cloud segmentation. Particle tracking is useful in areas such as Albuquerque, NM where clouds move in smaller fragments due to rapid variance in wind direction caused by nearby mountains. By combining neural networks and more classical technologies, the system becomes more robust and explainable then either image processing or pure neural network technologies, respectively.