**Author details**

Pedro Junior Zucatelli1 \*, Erick Giovani Sperandio Nascimento2 , Alex Álisson Bandeira Santos2 , Alejandro Mauricio Gutiérrez Arce3 and Davidson Martins Moreira<sup>2</sup>

1 Federal University of Espirito Santo—UFES, Vitória, Brazil

2 Manufacturing and Technology Integrated Campus—SENAI CIMATEC, Salvador, Brazil

3 Universidad de la Republica—UDELAR, Montevideo, Uruguay

\*Address all correspondence to: pedrojrzucatelli@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**233**

rser.2011.09.024

*Study of the Wind Speed Forecasting Applying Computational Intelligence*

chapter\_02/#figure\_26 [Accessed July,

[8] GWEC. Global Wind Statistics 2017, GWEC. 2018. Available from: http:// gwec.net/wp-content/uploads/vip/ GWEC\_PRstats2017\_EN-003\_FINAL.

[9] Rego EE, Ribeiro CO. Successful Brazilian experience for promoting wind energy generation. The Electricity

[10] ABEEólica. Annual Wind Energy Report 2018. ABEEólica, the Brazilian Wind Power Association. Available from: http://abeeolica.org.br/dadosexternos/ [Accessed July, 2019]

[11] Watts J. Uruguay Makes Dramatic Shift to Nearly 95% Electricity from Clean Energy: Keep it in the Ground Renewable Energy. 2015. Available from: https://www.theguardian.com/ environment/2015/dec/03/uruguaymakes-dramatic-shift-to-nearly-95 clean-energy [Accessed July, 2019]

[12] REN21. Renewables 2017 Global Status Report. REN21 Secretariat, Paris. 2017. Available from: http://www.ren21. net/gsr-2017/chapters/chapter\_01/ chapter\_01/ [Accessed July, 2019]

[13] Zucatelli PJ, Nascimento EGS, Aylas GYR, Souza NBP, Kitagawa YKL, Santos AAB, et al. Short-term wind speed forecasting in Uruguay using computational intelligence. Heliyon. 2019;**5**(5):e01664. DOI: 10.1016/j.

[14] Esmaeili MA, Twomey J. Selforganizing map (SOM) in wind speed forecasting: A new approach in computational intelligence (CI) forecasting methods. In: Proceedings of the ASME/ISCIE 2012 International Symposium on Flexible Automation,

heliyon.2019.e01664

Journal. 2018;**31**(2):13-17. DOI: 10.1016/j.tej.2018.02.003

pdf [Accessed July, 2019]

2019]

*DOI: http://dx.doi.org/10.5772/intechopen.89758*

Chaves GLD, Tosta MCR. The application, required investments and operational costs of geological CO2 sequestration: A case study. Research, Society and Development. 2019;**8**(6):1-28. DOI: 10.33448/rsd-v8i6.1023. ISSN 2525-3409

[1] Zucatelli PJ, Meneguelo AP,

**References**

[2] UNEP/GRID-Arendal. Global Atmospheric Concentration of CO2. UNEP/GRID-Arendal Maps and Graphics Library. 1999. Available from: http://www.grida.no/resources/5500

[3] NOAA. Global Greenhouse Gas Reference Network. 2019. Available from: https://www.esrl.noaa.gov/gmd/ ccgg/trends/graph.html [Accessed July,

[4] Abarzadeh M, Kojabadi HM, Chang L. Study of novel power electronic converters for small scale wind energy conversion systems. In: Cao W, Hu Y, editors. Renewable Energy—Utilisation and System Integration. Rijeka: IntechOpen; 2016. DOI: 10.5772/62477. Available from: https://www.intechopen. com/books/renewable-energyutilisation-and-system-integration/ study-of-novel-power-electronicconverters-for-small-scale-windenergy-conversion-systems

[5] IEA. Renewables. 2018. Available from: https://www.iea.org/topics/ renewables/ [Accessed July, 2019]

[6] Leung DYC, Yang Y. Wind energy development and its environmental impact: A review. Renewable and Sustainable Energy Reviews. 2012;**16**(1):1031-1039. DOI: 10.1016/j.

[7] Renewables. Global Status Report: 02 Market and Industry Trends. 2017. Available from: http://www.ren21. net/gsr-2017/chapters/chapter\_02/

[Accessed July, 2019]

2019]

*Study of the Wind Speed Forecasting Applying Computational Intelligence DOI: http://dx.doi.org/10.5772/intechopen.89758*
