  Solar energy is now the cheapest form of electricity in history.
Unfortunately, significantly increasing the grid's fraction of solar energy
remains challenging due to its variability, which makes balancing electricity's
supply and demand more difficult. While thermal generators' ramp rate -- the
maximum rate that they can change their output -- is finite, solar's ramp rate
is essentially infinite. Thus, accurate near-term solar forecasting, or
nowcasting, is important to provide advance warning to adjust thermal generator
output in response to solar variations to ensure a balanced supply and demand.
To address the problem, this paper develops a general model for solar
nowcasting from abundant and readily available multispectral satellite data
using self-supervised learning. Specifically, we develop deep auto-regressive
models using convolutional neural networks (CNN) and long short-term memory
networks (LSTM) that are globally trained across multiple locations to predict
raw future observations of the spatio-temporal data collected by the recently
launched GOES-R series of satellites. Our model estimates a location's future
solar irradiance based on satellite observations, which we feed to a regression
model trained on smaller site-specific solar data to provide near-term solar
photovoltaic (PV) forecasts that account for site-specific characteristics. We
evaluate our approach for different coverage areas and forecast horizons across
25 solar sites and show that our approach yields errors close to that of a
model using ground-truth observations.
