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Is the wind a periodical phenomenon? The case of Mexico

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  • Hernandez-Escobedo, Quetzalcoatl
  • Manzano-Agugliaro, Francisco
  • Gazquez-Parra, Jose Antonio
  • Zapata-Sierra, Antonio

Abstract

Under some author's opinion the wind is not a periodical phenomenon and therefore it is more reasonable to invest in renewable periodical energies as tides. In this paper we have developed a computer application based in MatLab©, that through the FFT (Fast Fourier Transform) analyzes the variations of wind speed amplitude in the time and frequency domain. The data were sampled every 10 min in the period 2000-2008. The data come from 31 Automatic Meteorological Stations (EMAs), the country of Mexico and correspond one per state. The survey shows the representation of spectral-temporal surfaces to long time intervals, as one year or more and denotes seasonal envelopes that alter the pattern at certain times everyday. As a conclusion, the wind has an important periodical component for the country of Mexico, since the fundamental component of the wind speed represents a frequency of 1/24 h-1 in a very accurate form throughout the time studied. To harness the wind potential of the country of Mexico it should be kept in mind that there is a minimum wind speed between 8 and 16 h and a maximum close to 24 h.

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  • Hernandez-Escobedo, Quetzalcoatl & Manzano-Agugliaro, Francisco & Gazquez-Parra, Jose Antonio & Zapata-Sierra, Antonio, 2011. "Is the wind a periodical phenomenon? The case of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 721-728, January.
    • Handle: RePEc:eee:rensus:v:15:y:2011:i:1:p:721-728
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    Cited by:

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    7. Nourani Esfetang, Naser & Kazemzadeh, Rasool, 2018. "A novel hybrid technique for prediction of electric power generation in wind farms based on WIPSO, neural network and wavelet transform," Energy, Elsevier, vol. 149(C), pages 662-674.
    8. Sagbansua, Lutfu & Balo, Figen, 2017. "Decision making model development in increasing wind farm energy efficiency," Renewable Energy, Elsevier, vol. 109(C), pages 354-362.
    9. Hernández-Escobedo, Q. & Rodríguez-García, E. & Saldaña-Flores, R. & Fernández-García, A. & Manzano-Agugliaro, F., 2015. "Solar energy resource assessment in Mexican states along the Gulf of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 216-238.
    10. Osmani, Atif & Zhang, Jun, 2014. "Optimal grid design and logistic planning for wind and biomass based renewable electricity supply chains under uncertainties," Energy, Elsevier, vol. 70(C), pages 514-528.
    11. Jiafu Yin & Dongmei Zhao, 2018. "Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment," Energies, MDPI, vol. 11(2), pages 1-18, February.
    12. Pérez-Denicia, Eduardo & Fernández-Luqueño, Fabián & Vilariño-Ayala, Darnes & Manuel Montaño-Zetina, Luis & Alfonso Maldonado-López, Luis, 2017. "Renewable energy sources for electricity generation in Mexico: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 597-613.
    13. Hufang Yang & Zaiping Jiang & Haiyan Lu, 2017. "A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series," Energies, MDPI, vol. 10(9), pages 1-30, September.
    14. Hernández-Escobedo, Q. & Saldaña-Flores, R. & Rodríguez-García, E.R. & Manzano-Agugliaro, F., 2014. "Wind energy resource in Northern Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 890-914.
    15. Nima Amjady & Oveis Abedinia, 2017. "Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine," Sustainability, MDPI, vol. 9(11), pages 1-18, November.
    16. Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
    17. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
    18. Ping Jiang & Zeng Wang & Kequan Zhang & Wendong Yang, 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting," Energies, MDPI, vol. 10(7), pages 1-29, July.
    19. Gang Wang & Dahai You & Zhe Zhang & Li Dai & Qi Zou & Hengwei Liu, 2018. "Network-Constrained Unit Commitment Based on Reserve Models Fully Considering the Stochastic Characteristics of Wind Power," Energies, MDPI, vol. 11(2), pages 1-20, February.
    20. Ye, Lin & Li, Yilin & Pei, Ming & Zhao, Yongning & Li, Zhuo & Lu, Peng, 2022. "A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching," Applied Energy, Elsevier, vol. 327(C).
    21. Haoran Zhao & Sen Guo & Huiru Zhao, 2018. "Comprehensive Performance Assessment on Various Battery Energy Storage Systems," Energies, MDPI, vol. 11(10), pages 1-26, October.
    22. Montoya, Francisco G. & García-Cruz, Amós & Montoya, Maria G. & Manzano-Agugliaro, Francisco, 2016. "Power quality techniques research worldwide: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 846-856.
    23. Alberto-Jesus Perea-Moreno & Gerardo Alcalá & Quetzalcoatl Hernandez-Escobedo, 2019. "Seasonal Wind Energy Characterization in the Gulf of Mexico," Energies, MDPI, vol. 13(1), pages 1-21, December.

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    FFT Wind speed Mexico Average day;

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