A compressive sensing framework for the analysis of solar Photo-Voltaic power

In this paper we propose a new stochastic model for solar Photo-Voltaic (PV) power that explicitly models the effect of cloud coverage as an attenuation of the two components that make up the deterministic solar irradiation pattern. Relying on compressive sensing methods we are able to fit a set of solar PV power data from California with the components of this stochastic model and extract the parameters of such a process thus effectively capturing the variability of solar power production. One can leverage the rich information coming from this parametric model for stochastic optimization and decision making.