The task of solar power forecasting becomes vital to ensure grid constancy and to enable an optimal unit commitment and cost-effective dispatch. Each year latest techniques and approaches appear to increase exactitude of models with the important goal of reducing uncertainty in the predictions. The aim of the paper is to compile a big part of the knowledge about solar power forcing, to focus on the most recent advancements and future trends. Firstly, the inspiration to achieve an accurate forecast is presented with the analysis of the economic implications it may have. To address the problem superlative prediction models are rummaged by us using machine learning techniques. We make a comparison between multiple regression techniques for creating prediction models, along with linear least squares and support vector machines using multiple kernel functions. Predictions are analyzed by us in our experiments for day ahead solar radiation data and it is shown that a machine learning approach yields feasible results for short-term solar prediction. The proposed model achieves a root mean square error improvement of around 29% compared to others proposed model except one.
Forecasting, short-term, SVR, renewable energy, machine learning