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Assessment of Resource and Forecast Modeling of Wind Speed through An Evolutionary Programming Approach for the North of Tehuantepec Isthmus (Cuauhtemotzin, Mexico)

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  • Luis M. López-Manrique

    (División Académica de Ingeniería y Arquitectura, Doctorado en Ciencias de la Ingeniería (DCI), DAIA-UJAT, Universidad Juárez Autónoma de Tabasco, Carret. Cunduacán-Jalpa de Méndez Km. 1, Unidad Chontalpa, Cunduacán 86690, Mexico
    División Académica de Ingeniería y Arquitectura, DAIA-UJAT, Universidad Juárez Autónoma de Tabasco, Carret. Cunduacán-Jalpa de Méndez Km. 1, Unidad Chontalpa, Cunduacán CP 86690, Mexico)

  • E. V. Macias-Melo

    (División Académica de Ingeniería y Arquitectura, DAIA-UJAT, Universidad Juárez Autónoma de Tabasco, Carret. Cunduacán-Jalpa de Méndez Km. 1, Unidad Chontalpa, Cunduacán CP 86690, Mexico)

  • O. May Tzuc

    (Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes, Apdo. Postal 150, Mérida 97310, Mexico)

  • A. Bassam

    (Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes, Apdo. Postal 150, Mérida 97310, Mexico)

  • K. M. Aguilar-Castro

    (División Académica de Ingeniería y Arquitectura, DAIA-UJAT, Universidad Juárez Autónoma de Tabasco, Carret. Cunduacán-Jalpa de Méndez Km. 1, Unidad Chontalpa, Cunduacán CP 86690, Mexico)

  • I. Hernández-Pérez

    (División Académica de Ingeniería y Arquitectura, DAIA-UJAT, Universidad Juárez Autónoma de Tabasco, Carret. Cunduacán-Jalpa de Méndez Km. 1, Unidad Chontalpa, Cunduacán CP 86690, Mexico)

Abstract

This work studies the characteristics of the wind resource for a location in the north zone of Tehuantepec isthmus. The study was conducted using climatic data from Cuauhtemotzin, Mexico, measured at different altitudes above the ground level. The measured data allowed establishing the profile of wind speeds as well as the analysis of its availability. Analysis results conclude that the behavior of the wind speed presents a bimodal distribution with dominant northeast wind direction (wind flow of sea–land). In addition, the area was identified as feasible for the use of low speed power wind turbines. On the other hand, the application of a new approach for very short-term wind speed forecast (10 min) applying multi-gene genetic programming and global sensitivity analysis is also presented. Using a computational methodology, an exogenous time series with fast computation time and good accuracy was developed for the forecast of the wind speed. The results presented in this work complement the panorama for the evaluation of the resource in an area recognized worldwide for its vast potential for wind power.

Suggested Citation

  • Luis M. López-Manrique & E. V. Macias-Melo & O. May Tzuc & A. Bassam & K. M. Aguilar-Castro & I. Hernández-Pérez, 2018. "Assessment of Resource and Forecast Modeling of Wind Speed through An Evolutionary Programming Approach for the North of Tehuantepec Isthmus (Cuauhtemotzin, Mexico)," Energies, MDPI, vol. 11(11), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3197-:d:183684
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    References listed on IDEAS

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    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. Li, Meishen & Li, Xianguo, 2005. "MEP-type distribution function: a better alternative to Weibull function for wind speed distributions," Renewable Energy, Elsevier, vol. 30(8), pages 1221-1240.
    3. Zhang, Yao & Wang, Jianxue, 2016. "K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1074-1080.
    4. Souma Chowdhury & Ali Mehmani & Jie Zhang & Achille Messac, 2016. "Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes," Energies, MDPI, vol. 9(5), pages 1-31, May.
    5. Aliashim Albani & Mohd Zamri Ibrahim, 2017. "Wind Energy Potential and Power Law Indexes Assessment for Selected Near-Coastal Sites in Malaysia," Energies, MDPI, vol. 10(3), pages 1-21, March.
    6. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    7. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    8. Yichao Liu & Daoyi Chen & Qian Yi & Sunwei Li, 2017. "Wind Profiles and Wave Spectra for Potential Wind Farms in South China Sea. Part I: Wind Speed Profile Model," Energies, MDPI, vol. 10(1), pages 1-24, January.
    9. Jaramillo, O.A. & Borja, M.A., 2004. "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case," Renewable Energy, Elsevier, vol. 29(10), pages 1613-1630.
    10. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    11. Yichao Liu & Sunwei Li & Qian Yi & Daoyi Chen, 2017. "Wind Profiles and Wave Spectra for Potential Wind Farms in South China Sea. Part II: Wave Spectrum Model," Energies, MDPI, vol. 10(1), pages 1-24, January.
    12. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    13. Dong, Yao & Wang, Jianzhou & Jiang, He & Shi, Xiaomeng, 2013. "Intelligent optimized wind resource assessment and wind turbines selection in Huitengxile of Inner Mongolia, China," Applied Energy, Elsevier, vol. 109(C), pages 239-253.
    14. Feiyu Zhang & Yuqi Dong & Kequan Zhang, 2016. "A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China," Sustainability, MDPI, vol. 8(6), pages 1-20, June.
    15. Hernández-Escobedo, Quetzalcoatl & Perea-Moreno, Alberto-Jesús & Manzano-Agugliaro, Francisco, 2018. "Wind energy research in Mexico," Renewable Energy, Elsevier, vol. 123(C), pages 719-729.
    16. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    17. Li, Gong & Shi, Jing & Zhou, Junyi, 2011. "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, Elsevier, vol. 36(1), pages 352-359.
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