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.

Pepe, D., Bianchini, G., Vicino, A. (2018). Estimating PV forecasting models from power data. In 2018 IEEE International Energy Conference, ENERGYCON 2018 (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/ENERGYCON.2018.8398827].

Estimating PV forecasting models from power data

Daniele Pepe;Gianni Bianchini
;
Antonio Vicino
2018-01-01

Abstract

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.
9781538636695
Pepe, D., Bianchini, G., Vicino, A. (2018). Estimating PV forecasting models from power data. In 2018 IEEE International Energy Conference, ENERGYCON 2018 (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/ENERGYCON.2018.8398827].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1060803