  Ahead-of-time forecasting of incident solar-irradiance on a panel is
indicative of expected energy yield and is essential for efficient grid
distribution and planning. Traditionally, these forecasts are based on
meteorological physics models whose parameters are tuned by coarse-grained
radiometric tiles sensed from geo-satellites. This research presents a novel
application of deep neural network approach to observe and estimate short-term
weather effects from videos. Specifically, we use time-lapsed videos
(sky-videos) obtained from upward facing wide-lensed cameras (sky-cameras) to
directly estimate and forecast solar irradiance. We introduce and present
results on two large publicly available datasets obtained from weather stations
in two regions of North America using relatively inexpensive optical hardware.
These datasets contain over a million images that span for 1 and 12 years
respectively, the largest such collection to our knowledge. Compared to
satellite based approaches, the proposed deep learning approach significantly
reduces the normalized mean-absolute-percentage error for both nowcasting, i.e.
prediction of the solar irradiance at the instance the frame is captured, as
well as forecasting, ahead-of-time irradiance prediction for a duration for
upto 4 hours.
