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

Abstract

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.

Keywords

• load forecasting
• sunlight
• solar power stations
• neural nets
• photovoltaic power systems
• learning (artificial intelligence)
• statistical analysis
• weather forecasting
• wind
• artificial neuronal network
• föhn
• south winds
• san juan
• argentina
• day-ahead solar irradiation curve
• extreme meteorological phenomena
• ann
• environmental variables
• mentioned phenomena
• calculated ideal solar irradiation curve
• methodology merges statistical learning methods
• numerical weather prediction methods
• raw forecast
• power production
• forecasting method
• solar energy production forecasting