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Smooth power generation from solar stations demand accurate, reliable andefficient forecast of solar energy for optimal integration to cater marketdemand; however, the implicit instability of solar energy production may causeserious problems for the smooth power generation. We report daily prediction ofsolar energy by exploiting the strength of machine learning techniques tocapture and analyze complicated behavior of enormous features effectively. Forthis purpose, dataset comprising of 98 solar stations has been taken fromenergy competition of American Meteorological Society (AMS) for predictingdaily solar energy. Forecast models of base line regressors including linear,ridge, lasso, decision tree, random forest and artificial neural networks havebeen implemented on the AMS solar dataset. Grid size is converted into twosections: 16x9 and 10x4 to ascertain attributes contributing more towards thegenerated power from densely located stations on global ensemble forecastsystem (GEFS). To evaluate the models, statistical measures of prediction errorin terms of RMSE, MAE and R2_score have been analyzed and compared with theexisting techniques. It has been observed that improved accuracy is achievedthrough random forest and ridge regressor for both grid sizes in contrast toall other proposed methods. Stability and reliability of the proposed schemesare evaluated on a single solar station as well as on multiple independentruns.

Short term solar energy prediction by machine learning algorithms