Chaotic dynamics applied in time prediction of photovoltaic production.

Abstract The advantage of accurate forecasts is that it solves the main problem related to renewable energies: their variability. Indeed, while renewable energies has not yet replaced fossil fuels, in spite of the efforts of many governments, it is because of their intermittent nature, hence the importance of prediction in this field. The new approach for energy prediction that we propose in this paper, is founded on the analysis of the dynamical behavior of the photovoltaic production of the Faculty of Sciences and Technology of Beni Mellal, Morocco. It consists in performing the phase space reconstruction, which allowed us later to build a database for the input of the neural network and thus take into account the dynamics of the system in the forecasting process. Then, in search of more precision, we introduce the wavelet transformation, to simplify the database constructed from phase space reconstruction. Finally, comparing between the predictions and the actual observations confirmed the efficiency of our approach. Highlights • PV production forecast has an major role in its integration into electricity mix and thus in the fight against climate change. • In this work, we propose a new approach for forecasting, based on the exploitation of the phase space reconstruction. • The proposed model is a hybrid method combining the phase space reconstruction, DWT method and recurrent neural network. • The method is tested on real observations to confirm the efficiency of our approach. • Comparison between predictions and actual data confirmed the effectiveness of our approach given the improved results. [ABSTRACT FROM AUTHOR]