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Modeling a robust wind-speed forecasting to apply to wind-energy production

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Abstract

To obtain green energy, it is important to know, in advance, an estimation of the weather conditions. In case of wind energy, another important factor is to determine the right moment to stop the turbine in case of strong winds to avoid its damage. This research introduces a tool, not only to increase green energy generation from wind, reducing CO2 emissions, but also to prevent failures in turbines that is especially interesting for manufacturers. Using Artificial Neural Networks and data from meteorological stations located in Gran Canaria airport and Tenerife Sur airport (both in Canary Islands, Spain), a robust prediction system able to determine wind speed with a mean absolute error of 0.29 m per second is presented.

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References

  1. World Bank (2018) Percentage of fossil fuel energy consumed by countries. http://datos.bancomundial.org/indicador/EG.USE.COMM.FO.ZS Accessed 24 Mar 2018

  2. Cinco D (2018) Spanish economic journal founded in 1978. In: News on the European pact of energy. http://cincodias.com/cincodias/2014/10/16/empresas/1413464789_240406.html. Accessed 24 Mar 2018

  3. European Commission (2018) Environment action programme to 2020. http://ec.europa.eu/environment/newprg/index.htm. Accessed 24 Mar 2018

  4. United Nations (2017) Climate summit 2015. http://www.cop21.gouv.fr/en/. Accessed 19 Apr 2017

  5. United Nations. Climate Summit 2015. http://www.un.org/sustainabledevelopment/cop21/ Accessed 24 March 2018

  6. Gorona del Viento (2018) Wind-hydro-pumped station of El Hierro. http://www.goronadelviento.es/index.php. Accessed 24 Mar 2018

  7. Xingpei L, Yibing L, Weidong X (2009) Wind speed prediction based on genetic neural network. In: 4th IEEE conference on industrial electronics and applications (ICIEA 2009), international conference center, Xi´an, P. R. China, 25–27 May 2009, pp 2448–2451. https://doi.org/10.1109/iciea.2009.5138642

  8. Zhao P, Xia J, Dai Y, He J (2010) Wind speed prediction using support vector regression. In: 5th IEEE conference on industrial electronics and applications (ICIEA 2010), Taichung, Taiwan, 15–17 June 2010, pp 882–886. https://doi.org/10.1109/pes.2010.5589418

  9. Tarade RS, Katti PK (2011) A comparative analysis for wind speed prediction. In: International conference on energy, automation, and signal (ICEAS). Siksha ‘O’ Anusandhan University Bhubaneswar, India, 28–30 December 2011, pp 1–6. https://doi.org/10.1109/iceas.2011.6147167

  10. Bhaskar K, Singh SN (2012) AWNN-assisted wind power forecasting using feed-forward neural network. IEEE Trans Sustain Energy 3(2):306–315. https://doi.org/10.1109/TSTE.2011.2182215

    Article  Google Scholar 

  11. Nan S, Su-quan Z, Xian-hui Z, Xun-wen S, Xiao-yan Z (2013) Wind speed forecasting based on grey predictor and genetic neural network models. In: International conference on measurement, information and control (ICMIC), vol 02. Harbin University of Science and Technology Building One No. 52 Xuefu Road Nangang District, Harbin, China, 16–18 August 2013, pp 1479–1482. https://doi.org/10.1109/mic.2013.6758238

  12. Chen N, Qian Z, Nabney IT, Meng X (2014) Wind power forecasts using gaussian processes and numerical weather prediction. IEEE Trans Power Syst 29(2):656–665. https://doi.org/10.1109/TPWRS.2013.2282366

    Article  Google Scholar 

  13. Yoshida S, Suzuki H, Kitajima T, Kassim AM, Yasuno T (2016) Correction method of wind speed prediction system using predicted wind speed fluctuation. In: 55th annual conference of the society of instrument and control engineers of Japan (SICE), Tsukuba International Congress Center, Tsukuba, Japan, 20–23 September 2016, pp 1054–1059. https://doi.org/10.1109/sice.2016.7749245

  14. Mert İ, Karakuş C, Üneş F (2016) Estimating the energy production of the wind turbine using artificial neural network. Neural Comput Appl 27:1231. https://doi.org/10.1007/s00521-015-1921-0

    Article  Google Scholar 

  15. Li J, Wang R, Zhang T (2016) Wind speed prediction using a cooperative coevolution genetic algorithm based on back propagation neural network. In: IEEE world congress on evolutionary computation (CEC), Vancouver, BC, 24–29 July 2016, pp 4578–4583. https://doi.org/10.1109/cec.2016.7744373

  16. Filik UB, Filik T (2017) Wind speed prediction using artificial neural networks based on multiple local measurements in Eskisehir. Energy Proc 107:264–269. https://doi.org/10.1016/j.egypro.2016.12.147

    Article  Google Scholar 

  17. Ulkat D, Günay ME (2017) Prediction of mean monthly wind speed and optimization of wind power by artificial neural networks using geographical and atmospheric variables: case of Aegean Region of Turkey. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2895-x

    Article  Google Scholar 

  18. Gamesa (2018) Case of study: Arinaga. In: A project come true. http://pdf.archiexpo.com/pdf/gamesa-electric/case-study-arinaga/88576-242524.html. Accessed 24 Mar 2018

  19. Gamesa (2018) off-shore turbine in Arinaga beats generation record. http://www.gamesacorp.com/es/cargarAplicacionNoticia.do?idCategoria=59&identificador=1042&urlAmigable=the-g128-50-mw-offshore-beats-power-generation-record-for-a-wind-turbine-in-spain.html. Accessed 24 Mar 2018

  20. Office of Energy Efficiency and Renewable Energy (2018) U.S. Department of Energy. How do wind turbines survive severe storms? https://www.energy.gov/eere/articles/how-do-wind-turbines-survive-severe-storms. Accessed 24 Mar 2018

  21. Professional Windsurfers Association (PWA) (2017) Events of the PWA world tour calendar 2017 http://www.pwaworldtour.com/index.php?id=2151. Accessed 18 Feb 2017

  22. Asociación Empresarial Eólica (2017) The Spanish Wind Energy Association. http://www.aeeolica.org/es/map/canarias/. Accessed 18 Feb 2017

  23. Haykin S (1999) Neural networks. In: A comprehensive foundation, 2nd edn. Prentice Hall Inc., United States of America

  24. Li X, Zecchin AC, Maier HR (2014) Selection of smoothing parameter estimators for general regression neural networks: applications to hydrological and water resources modelling. Environ Model Softw 59:162–186. https://doi.org/10.1016/j.envsoft.2014.05.010

    Article  Google Scholar 

  25. Bakker M, Vreeburg JHG, van Schagen KM, Rietveld LC (2013) A fully adaptive forecasting model for short-term drinking water demand. Environ Model Softw 48:141–151. https://doi.org/10.1016/j.envsoft.2013.06.012

    Article  Google Scholar 

  26. Valverde MC, Araujo E, Campos Velho H (2014) Neural network and fuzzy logic statistical downscaling of atmospheric circulation-type specific weather pattern for rainfall forecasting. Appl Soft Comput 22:681–694. https://doi.org/10.1016/j.asoc.2014.02.025

    Article  Google Scholar 

  27. Hernández-Travieso JG, Herrera-Jiménez AL, Travieso-González CM, Morgado-Dias F, Alonso-Hernández JB, Ravelo-García AG (2017) Temperature control by its forecasting applying score fusion for sustainable development. Sustainability 9:193

    Article  Google Scholar 

  28. Devi CJ, Reddy PBS, Kumar KV, Reddy BM, Nayak RN (2012) ANN approach for weather prediction using Backpropagation. Int J Eng Trends Technol 3:19–23

    Google Scholar 

  29. Serrano A, Soria E, Martín J (2009) Redes Neuronales Artificiales. Universidad de Valencia (Escuela Técnica Superior Ingeniería, Departamento de Ingeniería Electrónica), Valencia

    Google Scholar 

  30. Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13:253–263

    Google Scholar 

  31. Gamesa (2018) G52-850 KW Technical Information. http://www.wind-power-program.com/Library/Turbine%20leaflets/Gamesa/Gamesa%20G52%20850kw.pdf. Accessed 24 Mar 2018

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Acknowledgements

This work has been supported by Endesa Foundation and the University of Las Palmas Foundation under Grant “Programa Innova Canarias 2020”.

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Authors and Affiliations

  1. Signal and Communications Department, Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, sn, Ed. de Telecomunicación, Pabellón B, Despacho 111, E35017, Las Palmas de Gran Canaria, Spain

    José Gustavo Hernández-Travieso, Carlos M. Travieso-González, Jesús B. Alonso-Hernández & Antonio G. Ravelo-García

  2. Signal and Communications Department, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, sn, Ed. de Telecomunicación, Pabellón B, E35017, Las Palmas de Gran Canaria, Spain

    José Miguel Canino-Rodríguez

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  1. José Gustavo Hernández-Travieso
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  2. Carlos M. Travieso-González
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  3. Jesús B. Alonso-Hernández
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  4. José Miguel Canino-Rodríguez
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Correspondence to José Gustavo Hernández-Travieso.

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The authors declare that they have no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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Hernández-Travieso, J.G., Travieso-González, C.M., Alonso-Hernández, J.B. et al. Modeling a robust wind-speed forecasting to apply to wind-energy production. Neural Comput & Applic 31, 7891–7905 (2019). https://doi.org/10.1007/s00521-018-3619-6

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  • DOI: https://doi.org/10.1007/s00521-018-3619-6

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