Estimating PV forecasting models from power data

In the context of photovoltaic generation forecasting, we propose a method for the estimation of the parameters of the well-known PVUSA model of a PV plant. This problem is addressed in the common scenario where on-site measurements of meteorological variables (i.e. solar irradiance and temperature) are not available. The proposed approach efficiently exploits only power generation measurements and relies on a set of tests to detect a clear-sky condition. The devised algorithm is characterized by very low computational effort. Experimental validation is presented and forecasting performance is evaluated on real data.

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