Paper presented to the 3rd Southern African Solar Energy Conference, South Africa, 11-13 May, 2015.
In this work, we propose a benchmarking of supervised
machine learning techniques (neural networks, Gaussian
processes and support vector machines) in order to forecast the
Global Horizontal solar Irradiance (GHI). We also include in
this benchmark a simple linear autoregressive (AR) model as
well as a naive model based on persistence of the clear sky
index. The models are calibrated and validated with data from
Reunion Island (21.34°S ; 55.49°E). The main findings of this
work are, that for hour ahead solar forecasting, the machine
learning techniques slightly improve the performances
exhibited by the linear AR and the persistence model. These
nonlinear techniques start to outperform their simple
counterparts for forecasting horizons greater than one hour.
