  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.
