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Solar energy production forecasting through artificial neuronal networks, considering the Föhn, north and south winds in San Juan, Argentina

2019; Institution of Engineering and Technology; Volume: 2019; Issue: 18 Linguagem: Inglês

10.1049/joe.2018.9368

2051-3305

Luis Carlos Parra Raffán, A. A. Romero, Maximiliano Martínez,

Grey System Theory Applications

This study presents a method to predict a day-ahead solar irradiation curve, under extreme meteorological phenomena (Föhn, north and south winds), existing in the province of San Juan, Argentina. The proposed method is based on an artificial neuronal network (ANN) which is trained with a data set filtered by the environmental variables that characterise the mentioned phenomena. A previously calculated ideal solar irradiation curve is modified from the forecasts generated by the ANN. The proposed methodology merges statistical learning methods and numerical weather prediction (NWP) methods, typically used to improve upon the raw forecast of a NWP model. A reduction of the uncertainty in the power production of photovoltaic plants in San Juan can be achieved with the results of the proposed forecasting method.