SVM-based model over an AR model and a radial basis function
neural network (RBFNN) model by using data obtained from the
National Solar Radiation Database (NSRDB). The 2D transmissivity
and other meteorological variables, including sky cover, relative
humidity, and wind speed, are used as the inputs for different
models to predict the atmospheric transmissivity, which then is
converted to solar power according to the latitude of the site and
the time of the day. The composition of this paper is as follows.
Section 2 describes different SPP models. The novel 2D solar radiation
representation and data normalization are illustrated in
Section 3. Computer simulations are provided in Section 4 to
demonstrate the superiority of the SVM-based model and investigate
the factors that influence the performance of the model.
Section 5 provides concluding remarks of the paper.