With the increasing penetration of photovoltaic (PV) power system into grid, the problems caused by the fluctuation and intermittence of PV power output draw more interest. The power output fluctuation, in fact, impact the power system’s stability. For this reason, an accurate forecast of PV production is necessary to consider photovoltaic a reliable energy source. In order to verify the effectiveness of the forecast data, measured and predicted data in Catania (Italy) have compared and the errors have been calculated by means of the normalized Root Mean Square Error (nRMSE). Then an algorithm that allows to classify a day as variable, cloudy, slightly cloudy or clear has been implemented. Based on this classification, a maximum forecast error is determined. In this context, a neural network has been implemented, it allows to predict the nRMSE of a specific day knowing the percentages of the variable, cloudy, slightly cloudy or clear minutes of that day calculated on forecast data. Referring to Catania (Italy), experimental data are reported to demonstrate the potentiality of the adopted solutions.

Analysis of forecast errors for irradiance on the horizontal plane

TINA, Giuseppe Marco;
2011

Abstract

With the increasing penetration of photovoltaic (PV) power system into grid, the problems caused by the fluctuation and intermittence of PV power output draw more interest. The power output fluctuation, in fact, impact the power system’s stability. For this reason, an accurate forecast of PV production is necessary to consider photovoltaic a reliable energy source. In order to verify the effectiveness of the forecast data, measured and predicted data in Catania (Italy) have compared and the errors have been calculated by means of the normalized Root Mean Square Error (nRMSE). Then an algorithm that allows to classify a day as variable, cloudy, slightly cloudy or clear has been implemented. Based on this classification, a maximum forecast error is determined. In this context, a neural network has been implemented, it allows to predict the nRMSE of a specific day knowing the percentages of the variable, cloudy, slightly cloudy or clear minutes of that day calculated on forecast data. Referring to Catania (Italy), experimental data are reported to demonstrate the potentiality of the adopted solutions.
forecast; photovoltaic; classification
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/90766
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