  Smooth power generation from solar stations demand accurate, reliable and
efficient forecast of solar energy for optimal integration to cater market
demand; however, the implicit instability of solar energy production may cause
serious problems for the smooth power generation. We report daily prediction of
solar energy by exploiting the strength of machine learning techniques to
capture and analyze complicated behavior of enormous features effectively. For
this purpose, dataset comprising of 98 solar stations has been taken from
energy competition of American Meteorological Society (AMS) for predicting
daily solar energy. Forecast models of base line regressors including linear,
ridge, lasso, decision tree, random forest and artificial neural networks have
been implemented on the AMS solar dataset. Grid size is converted into two
sections: 16x9 and 10x4 to ascertain attributes contributing more towards the
generated power from densely located stations on global ensemble forecast
system (GEFS). To evaluate the models, statistical measures of prediction error
in terms of RMSE, MAE and R2_score have been analyzed and compared with the
existing techniques. It has been observed that improved accuracy is achieved
through random forest and ridge regressor for both grid sizes in contrast to
all other proposed methods. Stability and reliability of the proposed schemes
are evaluated on a single solar station as well as on multiple independent
runs.
