An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay

P.J. Zucatelli, E.G.S. Nascimento, A.Á.B. Santos, A.M.G. Arce and D.M. Moreira

Energy, 2021, vol. 230, issue C

Abstract: Large variations in wind energy production over a period of minutes to hours is a challenge for electricity balancing authorities. The use of reliable tools for the prediction of wind power and wind power ramp events is essential for the operator of the electrical system. The main objective of this work is to analyze the wind power and wind power ramp forecasting at Brazil and Uruguay. To achieve this goal the wavelet decomposition applying 48 different mother wavelet functions and deep learning techniques are used. The recurrent neural network was trained to perform the forecasting of 1 h ahead, and then, using it, the trained network was applied to recursively infer the forecasting for the next hours of the wind speed. After this computational procedure, the wind power and the wind power ramp were predicted. The results showed good accuracy and can be used as a tool to help national grid operators for the energy supply. The wavelet discrete Meyer family (dmey) demonstrates greater precision in the decomposition of the wind speed signals. Therefore, it is proven that the wavelet dmey is the most accurate in the decomposition of temporal wind signals, whether using signals from tropical or subtropical regions.

Keywords: Computational methods; Computer science; Deep learning; Engineering science and mechanics; Sustainable energy; Wind engineering (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221010902
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:230:y:2021:i:c:s0360544221010902

DOI: 10.1016/j.energy.2021.120842

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().