Prediction of Time Series of Photovoltaic Energy Production Using Artificial Neural Networks

Autor: A. Barhdadi, A. Lfakir, Abderrazzak Elamim, Philippe Thevenin, Amine Haibaoui, Bouchaib Hartiti
Rok vydání: 2019
Předmět:
Zdroj: Advances in Intelligent Systems and Computing ISBN: 9783030120641
DOI: 10.1007/978-3-030-12065-8_23
Popis: An artificial neural network (ANN) model is used for forecasting the power provided by photovoltaic solar panels using feed forward neural network (FFNN) of a photovoltaic installation located in the city of Mohammedia (Morocco). One year of hourly data on solar irradiance, ambient temperature and output PV power were available for this study. For this, different combinations of inputs with different numbers of hidden neurons were considered. To evaluate this model several statistic parameters were used such, as the coefficient of determination (R2), the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE). The results of this model, tested on unknown data, showed that the model works well, with determination coefficients lying between 0.98 and 0.998 for sunny days and between 0.82 and 0.96 for cloudy days.
Databáze: OpenAIRE