Abstract
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.
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The authors would like to thank “Institute for Research in Solar Energy and New Energies (IRESEN)” for the financing of the project PROPRE.MA.
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Elamim, A., Hartiti, B., Barhdadi, A., Haibaoui, A., Lfakir, A., Thevenin, P. (2019). Prediction of Time Series of Photovoltaic Energy Production Using Artificial Neural Networks. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 912. Springer, Cham. https://doi.org/10.1007/978-3-030-12065-8_23
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DOI: https://doi.org/10.1007/978-3-030-12065-8_23
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