  With the recent interest in net-zero sustainability for commercial buildings,
integration of photovoltaic (PV) assets becomes even more important. This
integration remains a challenge due to high solar variability and uncertainty
in the prediction of PV output. Most existing methods predict PV output using
either local power/weather history or global weather forecasts, thereby
ignoring either the impending global phenomena or the relevant local
characteristics, respectively. This work proposes to leverage weather data from
both local weather history and global forecasts based on time series modeling
with exogenous inputs. The proposed model results in eighteen hour ahead
forecasts with a mean accuracy of $\approx$ 80\% and uses data from the
National Ocean and Atmospheric Administration's (NOAA) High-Resolution Rapid
Refresh (HRRR) model.
