IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v15y2011i1p721-728.html
   My bibliography  Save this article

Is the wind a periodical phenomenon? The case of Mexico

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364-0321(10)00310-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wichser, Corinne & Klink, Katherine, 2008. "Low wind speed turbines and wind power potential in Minnesota, USA," Renewable Energy, Elsevier, vol. 33(8), pages 1749-1758.
    2. Sebitosi, A.B. & Pillay, P., 2008. "Renewable energy and the environment in South Africa: A way forward," Energy Policy, Elsevier, vol. 36(9), pages 3312-3316, September.
    3. Himri, Y. & Rehman, S. & Draoui, B. & Himri, S., 2008. "Wind power potential assessment for three locations in Algeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2495-2504, December.
    4. Cadenas, Erasmo & Rivera, Wilfrido, 2009. "Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks," Renewable Energy, Elsevier, vol. 34(1), pages 274-278.
    5. Cancino-Solórzano, Yoreley & Xiberta-Bernat, Jorge, 2009. "Statistical analysis of wind power in the region of Veracruz (Mexico)," Renewable Energy, Elsevier, vol. 34(6), pages 1628-1634.
    6. Hall, Peter J. & Bain, Euan J., 2008. "Energy-storage technologies and electricity generation," Energy Policy, Elsevier, vol. 36(12), pages 4352-4355, December.
    7. Denny, Eleanor, 2009. "The economics of tidal energy," Energy Policy, Elsevier, vol. 37(5), pages 1914-1924, May.
    8. Sebitosi, A.B., 2008. "Energy efficiency, security of supply and the environment in South Africa: Moving beyond the strategy documents," Energy, Elsevier, vol. 33(11), pages 1591-1596.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    2. Croonenbroeck, Carsten & Dahl, Christian Møller, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Energy, Elsevier, vol. 73(C), pages 221-232.
    3. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    4. 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.
    5. Cruz-Peragon, F. & Palomar, J.M. & Casanova, P.J. & Dorado, M.P. & Manzano-Agugliaro, F., 2012. "Characterization of solar flat plate collectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1709-1720.
    6. Putz, Dominik & Gumhalter, Michael & Auer, Hans, 2021. "A novel approach to multi-horizon wind power forecasting based on deep neural architecture," Renewable Energy, Elsevier, vol. 178(C), pages 494-505.
    7. 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.
    8. Alemán-Nava, Gibrán S. & Casiano-Flores, Victor H. & Cárdenas-Chávez, Diana L. & Díaz-Chavez, Rocío & Scarlat, Nicolae & Mahlknecht, Jürgen & Dallemand, Jean-Francois & Parra, Roberto, 2014. "Renewable energy research progress in Mexico: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 140-153.
    9. 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.
    10. 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.
    11. 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.
    12. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
    13. 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.
    14. Sagbansua, Lutfu & Balo, Figen, 2017. "Decision making model development in increasing wind farm energy efficiency," Renewable Energy, Elsevier, vol. 109(C), pages 354-362.
    15. 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.
    16. 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.
    17. 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).
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hernández-Escobedo, Q. & Manzano-Agugliaro, F. & Zapata-Sierra, A., 2010. "The wind power of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2830-2840, December.
    2. El Alimi, Souheil & Maatallah, Taher & Dahmouni, Anouar Wajdi & Ben Nasrallah, Sassi, 2012. "Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5466-5478.
    3. Liu, Hui & Chen, Chao & Tian, Hong-qi & Li, Yan-fei, 2012. "A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks," Renewable Energy, Elsevier, vol. 48(C), pages 545-556.
    4. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    5. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
    6. Morgan Bazilian & Patrick Nussbaumer & Hans-Holger Rogner & Abeeku Brew-Hammond & Vivien Foster & Shonali Pachauri & Eric Williams & Mark Howells & Philippe Niyongabo & Lawrence Musaba & Brian Ó Galla, 2011. "Energy Access Scenarios to 2030 for the Power Sector in Sub-Saharan Africa," Working Papers 2011.68, Fondazione Eni Enrico Mattei.
    7. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    8. Wade, N.S. & Taylor, P.C. & Lang, P.D. & Jones, P.R., 2010. "Evaluating the benefits of an electrical energy storage system in a future smart grid," Energy Policy, Elsevier, vol. 38(11), pages 7180-7188, November.
    9. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    10. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    11. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    12. Rabiee, Abdorreza & Khorramdel, Hossein & Aghaei, Jamshid, 2013. "A review of energy storage systems in microgrids with wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 316-326.
    13. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    14. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
    15. Eva Segura & Rafael Morales & José A. Somolinos, 2019. "Increasing the Competitiveness of Tidal Systems by Means of the Improvement of Installation and Maintenance Maneuvers in First Generation Tidal Energy Converters—An Economic Argumentation," Energies, MDPI, vol. 12(13), pages 1-27, June.
    16. Zamora, Ramon & Srivastava, Anurag K., 2010. "Controls for microgrids with storage: Review, challenges, and research needs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 2009-2018, September.
    17. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    18. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    19. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
    20. Shahriyar Nasirov & Carlos Silva & Claudio A. Agostini, 2015. "Investors’ Perspectives on Barriers to the Deployment of Renewable Energy Sources in Chile," Energies, MDPI, vol. 8(5), pages 1-21, April.

    More about this item

    Keywords

    FFT Wind speed Mexico Average day;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:15:y:2011:i:1:p:721-728. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.