Paper Title
Machine Learning Methods for Solar Radiation Forecasting: A Review

Abstract - The Sun is an ultimate source of energy for all the objects living on Earth and solar radiation estimation is utmost important as this radiation serves as a primary energy source of conversion for the photovoltaic panels and the solar thermal power plants. This solar radiation is not constant in every region but it depends on various climatological parameters like temperature, wind-speed and many more, so there is intermittency in its behavior which results in changes in the electrical energy production. The above few statements reveal the necessity of predicting solar radiation and in the upcoming sections of the paper it will be seen how the researchers overseas have tried to accomplish it.Physical method, Statistical method, Hybrid method are namely the methods put forward by the researchers around the globe for the purpose of forecasting Global Solar Radiation. With this background, it is clear that the paper is now going to focuses only on the various machine learning techniques used till date with a very brief recapitulation of each algorithm. The time-series forecasting method (ARIMA, ARX, AR models) are the oldest of all the methods thus there are countless research materials on them but in the recent years forecasting with the help of Machine Learning techniques (k-NN, SVM, Random Forest) have gained popularity over the time series method of forecasting. Using the Machine Learning technique will not only digitalize the solar radiation forecast but also will play a very important role in bringing the solar panels a step ahead in the reserve markets w.r.t participation and will lead to reduction in cost. Keywords - Intermittency, k-NN, Machine Learning, Estimating, Solar Radiation