  As renewable distributed energy resources (DERs) penetrate the power grid at
an accelerating speed, it is essential for operators to have accurate solar
photovoltaic (PV) energy forecasting for efficient operations and planning.
Generally, observed weather data are applied in the solar PV generation
forecasting model while in practice the energy forecasting is based on
forecasted weather data. In this paper, a study on the uncertainty in weather
forecasting for the most commonly used weather variables is presented. The
forecasted weather data for six days ahead is compared with the observed data
and the results of analysis are quantified by statistical metrics. In addition,
the most influential weather predictors in energy forecasting model are
selected. The performance of historical and observed weather data errors is
assessed using a solar PV generation forecasting model. Finally, a sensitivity
test is performed to identify the influential weather variables whose accurate
values can significantly improve the results of energy forecasting.

Machine Learning