In this paper, a simple but accurate approach for short-term forecasting of the power produced by a Large-Scale Grid Connected Photovoltaic Plant (LS-GCPV) is presented. A 1-year database of solar irradiance, cell temperature and power output produced by a 1-MWp photovoltaic plant located in Southern Italy is used for developing three distinct artificial neural network (ANN) models, to be applied to three typical types of day (sunny, partly cloudy and overcast). The possibility of obtaining accurate results by using solely the monitored data rather than knowing the actual architecture and details of the plant is a notable advantage; in particular, the method’s reliability gives to operation and maintenance and to grid operators excellent confidence in the evaluation of the performance of the plant and in the programming of the dispatching plans, respectively.

Short-term forecasting of power production in a large-scale photovoltaic plant

MASSI PAVAN, ALESSANDRO;LUGHI, VANNI
2014-01-01

Abstract

In this paper, a simple but accurate approach for short-term forecasting of the power produced by a Large-Scale Grid Connected Photovoltaic Plant (LS-GCPV) is presented. A 1-year database of solar irradiance, cell temperature and power output produced by a 1-MWp photovoltaic plant located in Southern Italy is used for developing three distinct artificial neural network (ANN) models, to be applied to three typical types of day (sunny, partly cloudy and overcast). The possibility of obtaining accurate results by using solely the monitored data rather than knowing the actual architecture and details of the plant is a notable advantage; in particular, the method’s reliability gives to operation and maintenance and to grid operators excellent confidence in the evaluation of the performance of the plant and in the programming of the dispatching plans, respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2827932
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