Illuminance prediction through SVM regression

In a scenario where renewable energies will play a foreground role, a reliable forecast of the energy production of such sources, like solar radiation, is a requirement for managing smart grids. However, the ability to predict the possibility to produce sustainable energy in different climatic conditions can be very useful for many other purposes (e.g., for Climate Sensitive Buildings). This is particularly true when working with climatic data that are, as a matter of fact, highly unsteady. Nevertheless, the use of data collected in the past can help to face the daily and seasonal variability. An algorithm for illuminance prediction based on Support Vector Regression (SVR) is here proposed and the results are presented and discussed.