ABSTRACT
Solar energy is now the cheapest form of electricity in history. Unfortunately,
signi.cantly increasing the electric grid’s fraction of
solar energy remains challenging due to its variability, which makes
balancing electricity’s supply and demand more di.cult. While
thermal generators’ ramp rate—the maximum rate at which they
can change their energy generation—is .nite, solar energy’s ramp
rate is essentially in.nite. Thus, accurate near-term solar forecasting,
or nowcasting, is important to provide advance warnings to
adjust thermal generator output in response to variations in solar
generation 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.
Speci.cally, 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 spectral
data collected by the recently launched GOES-R series of satellites.
Our model estimates a location’s near-term future solar irradiance
based on satellite observations, which we feed to a regression model
trained on smaller site-speci.c solar data to provide near-term solar
photovoltaic (PV) forecasts that account for site-speci.c characteristics.
We evaluate our approach for di.erent coverage areas and
forecast horizons across 25 solar sites and show that it yields errors
close to that of a model using ground-truth observations
