IDEAS home Printed from https://ideas.repec.org/a/gam/jcltec/v5y2023i2p37-765d1162696.html
   My bibliography  Save this article

Forecast Optimization of Wind Speed in the North Coast of the Yucatan Peninsula, Using the Single and Double Exponential Method

Author

Listed:
  • Christy Pérez-Albornoz

    (Department of Renewable Energy, Centro de Investigación Científica de Yucatan (CICY), Mérida 97205, Mexico
    These authors contributed equally to this work.)

  • Ángel Hernández-Gómez

    (School of Sciences, Universidad Autónoma de San Luis Potosí (UASLP), San Luis Potosi 78000, Mexico
    These authors contributed equally to this work.)

  • Victor Ramirez

    (Department of Renewable Energy, Centro de Investigación Científica de Yucatan (CICY), Mérida 97205, Mexico
    Consejo Nacional de Ciencia y Tecnología (CONACYT), Ciudad de México 03940, Mexico)

  • Damien Guilbert

    (Group of Research in Electrical Engineering of Nancy (GREEN), Université de Lorraine, F-54000 Nancy, France)

Abstract

Installation of new wind farms in areas such as the north coast of the Yucatan peninsula is of vital importance to face the local energy demand. For the proper functioning of these facilities it is important to perform wind data analysis, the data having been collected by anemometers, and to consider the particular characteristics of the studied area. However, despite the great development of anemometers, forecasting methods are necessary for the optimal harvesting of wind energy. For this reason, this study focuses on developing an enhanced wind forecasting method that can be applied to wind data from the north coast of the Yucatan peninsula (in general, any type of data). Thus, strategies can be established to generate a greater amount of energy from the wind farms, which supports the local economy of this area. Four variants have been developed based on the traditional double and single exponential methods. Furthermore, these methods were compared to the experimental data to obtain the optimal forecasting method for the Yucatan area. The forecasting method with the highest performance has obtained an average relative error of 7.9510% and an average mean error of 0.3860 m/s.

Suggested Citation

  • Christy Pérez-Albornoz & Ángel Hernández-Gómez & Victor Ramirez & Damien Guilbert, 2023. "Forecast Optimization of Wind Speed in the North Coast of the Yucatan Peninsula, Using the Single and Double Exponential Method," Clean Technol., MDPI, vol. 5(2), pages 1-22, June.
    • Handle: RePEc:gam:jcltec:v:5:y:2023:i:2:p:37-765:d:1162696
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-8797/5/2/37/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-8797/5/2/37/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dong, Yunxuan & Wang, Jing & Xiao, Ling & Fu, Tonglin, 2021. "Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target," Energy, Elsevier, vol. 215(PB).
    2. Cadenas, E. & Jaramillo, O.A. & Rivera, W., 2010. "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renewable Energy, Elsevier, vol. 35(5), pages 925-930.
    3. David Barbosa de Alencar & Carolina De Mattos Affonso & Roberto Célio Limão de Oliveira & Jorge Laureano Moya Rodríguez & Jandecy Cabral Leite & José Carlos Reston Filho, 2017. "Different Models for Forecasting Wind Power Generation: Case Study," Energies, MDPI, vol. 10(12), pages 1-27, November.
    4. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
    5. Seong, Byeongchan, 2020. "Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models," Economic Modelling, Elsevier, vol. 91(C), pages 463-468.
    6. Jiang, Ping & Wang, Biao & Li, Hongmin & Lu, Haiyan, 2019. "Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting," Energy, Elsevier, vol. 173(C), pages 468-482.
    7. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    8. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    9. 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.
    10. Jung, Jaesung & Tam, Kwa-Sur, 2013. "A frequency domain approach to characterize and analyze wind speed patterns," Applied Energy, Elsevier, vol. 103(C), pages 435-443.
    11. Majidi Nezhad, M. & Heydari, A. & Pirshayan, E. & Groppi, D. & Astiaso Garcia, D., 2021. "A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method," Renewable Energy, Elsevier, vol. 179(C), pages 2198-2211.
    Full references (including those not matched with items on IDEAS)

    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. Yitian Xing & Fue-Sang Lien & William Melek & Eugene Yee, 2022. "A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model," Energies, MDPI, vol. 15(15), pages 1-35, July.
    2. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
    3. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
    4. Manisha Sawant & Rupali Patil & Tanmay Shikhare & Shreyas Nagle & Sakshi Chavan & Shivang Negi & Neeraj Dhanraj Bokde, 2022. "A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(21), pages 1-24, October.
    5. Escalante Soberanis, M.A. & Mérida, W., 2015. "Regarding the influence of the Van der Hoven spectrum on wind energy applications in the meteorological mesoscale and microscale," Renewable Energy, Elsevier, vol. 81(C), pages 286-292.
    6. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    7. Chia-Sheng Tu & Chih-Ming Hong & Hsi-Shan Huang & Chiung-Hsing Chen, 2020. "Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine," Energies, MDPI, vol. 13(23), pages 1-18, November.
    8. Shahram Hanifi & Saeid Lotfian & Hossein Zare-Behtash & Andrea Cammarano, 2022. "Offshore Wind Power Forecasting—A New Hyperparameter Optimisation Algorithm for Deep Learning Models," Energies, MDPI, vol. 15(19), pages 1-21, September.
    9. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    10. Kübra Tümay Ateş, 2023. "Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    11. Xu, Li & Ou, Yanxia & Cai, Jingjing & Wang, Jin & Fu, Yang & Bian, Xiaoyan, 2023. "Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition," Renewable Energy, Elsevier, vol. 216(C).
    12. Masseran, Nurulkamal, 2016. "Modeling the fluctuations of wind speed data by considering their mean and volatility effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 777-784.
    13. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
    14. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
    15. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    16. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Elsevier, vol. 179(C), pages 2174-2186.
    17. Ke Zhang & Xiao Li & Jie Su, 2022. "Variable Support Segment-Based Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 15(11), pages 1-18, June.
    18. Zhihao Shang & Quan Wen & Yanhua Chen & Bing Zhou & Mingliang Xu, 2022. "Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion," Energies, MDPI, vol. 15(8), pages 1-23, April.
    19. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    20. Ju-Yeol Ryu & Bora Lee & Sungho Park & Seonghyeon Hwang & Hyemin Park & Changhyeong Lee & Dohyeon Kwon, 2022. "Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models," Energies, MDPI, vol. 15(24), pages 1-14, December.

    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:gam:jcltec:v:5:y:2023:i:2:p:37-765:d:1162696. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.