Localized solar power prediction based on weather data from local history and global forecasts
For commercial building operators, this work improves solar power prediction by integrating both local and global data sources, addressing a practical challenge in net-zero energy management.
The paper proposes a hybrid time series model that combines local weather history and global forecasts to predict photovoltaic (PV) power output 18 hours ahead, achieving approximately 80% mean accuracy.
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.