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Weather is one of the main problems in implementing forecasts forphotovoltaic panel systems. Since it is the main generator of disturbances andinterruptions in electrical energy. It is necessary to choose a reliableforecasting model for better energy use. A measurement prototype wasconstructed in this work, which collects in-situ voltage and currentmeasurements and the environmental factors of radiation, temperature, andhumidity. Subsequently, a correlation analysis of the variables and theimplementation of artificial neural networks were performed to perform thesystem forecast. The best estimate was the one made with three variables(lighting, temperature, and humidity), obtaining an error of 0.255. Theseresults show that it is possible to make a good estimate for a photovoltaicpanel system.

Photo-Voltaic Panel Power Production Estimation with an Artificial Neural Network using Environmental and Electrical Measurements