  Nowadays, with the unprecedented penetration of renewable distributed energy
resources (DERs), the necessity of an efficient energy forecasting model is
more demanding than before. Generally, forecasting models are trained using
observed weather data while the trained models are applied for energy
forecasting using forecasted weather data. In this study, the performance of
several commonly used forecasting methods in the presence of weather predictors
with uncertainty is assessed and compared. Accordingly, both observed and
forecasted weather data are collected, then the influential predictors for
solar PV generation forecasting model are selected using several measures.
Using observed and forecasted weather data, an analysis on the uncertainty of
weather variables is represented by MAE and bootstrapping. The energy
forecasting model is trained using observed weather data, and finally, the
performance of several commonly used forecasting methods in solar energy
forecasting is simulated and compared for a real case study.
