  Power systems engineers are actively developing larger power plants out of
photovoltaics imposing some major challenges which include its intermittent
power generation and its poor dispatchability. The issue is that PV is a
variable generation source unless additional planning and system additions for
mitigation of generation intermittencies. One underlying factor that can
enhance the applications around mitigating distributed energy resource
intermittency challenges is forecasting the generation output. This is
challenging especially with renewable energy sources which are weather
dependent as due to the random nature of weather variance. This work puts forth
a forecasting model which uses the solar variables to produce a PV generation
forecast and evaluates a set of machine learning models for this task. In this
paper, a forecaster for irradiance prediction for intra-day is proposed. This
forecaster is capable of forecasting 15 minutes and hourly irradiance up to one
week ahead. The paper performed a correlation and sensitivity analysis of the
strength of the relationship between local weather parameters and system
generation. In this study performance of SVM, CART, ANN, and Ensemble learning
were analyzed for the prediction of 15-minute intraday and day-ahead
irradiance. The results show that SVM and Ensemble learning yielded the lowest
MAE for 15-minute intraday and day-ahead irradiance, respectively.
