Modeling and control of a solar power plant using support vector learning is considered in this work. The model is based on a radial basis function network architecture and uses subtractive clustering and support vector learning to find the parameters and size of the network. To achieve a more interpretable structure the proposed method proceeds in two phases. Firstly, the input-output data is clustered according to the subtractive clustering method. Secondly, the support vector learning algorithm finds the number and location of centers and the weights of the network. This approach will improve the interpretability analysis and reduces the complexity of the problem. The proposed learning scheme is applied to the distributed collector field of a solar power plant. An internal model control scheme is also suggested, in which the proposed strategy proves to be effective in modeling the plant dynamics and the corresponding inverse