This paper presents a parametric model approach to address the problem of photovoltaic generation forecasting in a scenario where measurements of meteorological variables, i.e., solar irradiance and temperature, are not available at the plant site. This scenario is relevant to electricity network operation, when a large number of PV plants are deployed in the grid. The proposed method makes use of raw cloud cover data provided by a meteorological service combined with power generation measurements, and is particularly suitable in PV plant integration on a large-scale basis, due to low model complexity and computational efficiency. An extensive validation is performed using both simulated and real data.

Pepe, D., Bianchini, G., Vicino, A. (2017). Model estimation for solar generation forecasting using cloud cover data. SOLAR ENERGY, 157, 1032-1046 [10.1016/j.solener.2017.08.086].

Model estimation for solar generation forecasting using cloud cover data

PEPE, DANIELE;BIANCHINI, GIANNI;VICINO, ANTONIO
2017-01-01

Abstract

This paper presents a parametric model approach to address the problem of photovoltaic generation forecasting in a scenario where measurements of meteorological variables, i.e., solar irradiance and temperature, are not available at the plant site. This scenario is relevant to electricity network operation, when a large number of PV plants are deployed in the grid. The proposed method makes use of raw cloud cover data provided by a meteorological service combined with power generation measurements, and is particularly suitable in PV plant integration on a large-scale basis, due to low model complexity and computational efficiency. An extensive validation is performed using both simulated and real data.
Pepe, D., Bianchini, G., Vicino, A. (2017). Model estimation for solar generation forecasting using cloud cover data. SOLAR ENERGY, 157, 1032-1046 [10.1016/j.solener.2017.08.086].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1014914