Estimation of Photovoltaic Generation Forecasting Models using Limited Information
It addresses the practical problem of PV forecasting for plant operators lacking local weather sensors, offering a computationally efficient solution.
This work proposes a low-complexity algorithm to estimate photovoltaic generation forecasting models without on-site meteorological measurements, using only power data, clear-sky irradiance, and temperature forecasts. Experimental validation on real data demonstrates its effectiveness.
This work deals with the problem of estimating a photovoltaic generation forecasting model in scenarios where measurements of meteorological variables (i.e. solar irradiance and temperature) at the plant site are not available. A novel algorithm for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant is proposed. Such a method is characterized by a low computational complexity, and efficiently exploits only power generation measurements, a theoretical clear-sky irradiance model, and temperature forecasts provided by a meteorological service. An extensive experimental validation of the proposed method on real data is also presented.