Chaotic dynamics applied in time prediction of photovoltaic production

Hasnaa Bazine and Mustapha Mabrouki

Renewable Energy, 2019, vol. 136, issue C, 1255-1265

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.

Keywords: Chaotic time series; Phase space reconstruction; Second generation wavelets; Recurrent neural network (RNN); Renewable energy forecasting; Power planning (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:136:y:2019:i:c:p:1255-1265

DOI: 10.1016/j.renene.2018.09.098

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