IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i2p356-d130085.html
   My bibliography  Save this article

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Author

Listed:
  • Dongbum Kang

    (Multidisciplinary Graduate School Program for Wind Energy, Graduate School, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea)

  • Kyungnam Ko

    (Faculty of Wind Energy Engineering, Graduate School, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea)

  • Jongchul Huh

    (Department of Mechanical Engineering, College of Engineering, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea)

Abstract

On Jeju Island, South Korea, an investigation was conducted to determine the best method for estimating Weibull parameters. Six methods commonly used in many fields of the wind energy industry were reviewed: the empirical, moment, graphical, energy pattern factor, maximum likelihood, and modified maximum likelihood methods. In order to improve the reliability of a research result, five-year actual wind speed data taken from nine sites with various topographical conditions were used for the estimation. Furthermore, the effect of various topographical conditions on the accuracy of the methods was analyzed and 10 bin interval types were applied to determine the most appropriate bin interval based on their performances. Weibull distributions that were estimated using these methods were compared with the observed wind speed distribution. Then the accuracy of each method was evaluated using four accuracy tests. The results showed that of the six methods, the moment method had the best performance regardless of topographical conditions, while the graphical method performed the worst. Additionally, topographical conditions did not affect the accuracy ranking of the methods for estimating the Weibull parameters, while an increase of terrain complexity resulted in an increase of discrepancy between the estimated Weibull distribution and the frequency of the observed wind speed data. In addition, the choice in bin interval greatly affected the accuracy of the graphical method while it did not depend on the accuracy of the modified maximum likelihood method.

Suggested Citation

  • Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:356-:d:130085
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/2/356/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/2/356/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado & Andrés Felipe Obando-Montaño, 2014. "An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)," Energies, MDPI, vol. 7(4), pages 1-25, April.
    2. Arslan, Talha & Bulut, Y. Murat & Altın Yavuz, Arzu, 2014. "Comparative study of numerical methods for determining Weibull parameters for wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 820-825.
    3. 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.
    4. Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network," Energies, MDPI, vol. 10(11), pages 1-17, October.
    5. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    6. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
    7. Gualtieri, Giovanni & Secci, Sauro, 2012. "Methods to extrapolate wind resource to the turbine hub height based on power law: A 1-h wind speed vs. Weibull distribution extrapolation comparison," Renewable Energy, Elsevier, vol. 43(C), pages 183-200.
    8. Lun, Isaac Y.F & Lam, Joseph C, 2000. "A study of Weibull parameters using long-term wind observations," Renewable Energy, Elsevier, vol. 20(2), pages 145-153.
    9. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.
    10. Ko, Kyungnam & Kim, Kyoungbo & Huh, Jongchul, 2010. "Variations of wind speed in time on Jeju Island, Korea," Energy, Elsevier, vol. 35(8), pages 3381-3387.
    11. Saleh, H. & Abou El-Azm Aly, A. & Abdel-Hady, S., 2012. "Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt," Energy, Elsevier, vol. 44(1), pages 710-719.
    12. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
    13. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2017. "Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data," Energies, MDPI, vol. 10(9), pages 1-24, September.
    14. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    15. Emilio Gómez-Lázaro & María C. Bueso & Mathieu Kessler & Sergio Martín-Martínez & Jie Zhang & Bri-Mathias Hodge & Angel Molina-García, 2016. "Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures," Energies, MDPI, vol. 9(2), pages 1-15, February.
    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. Kim, Gyeongmin & Hur, Jin, 2023. "A probabilistic approach to potential estimation of renewable energy resources based on augmented spatial interpolation," Energy, Elsevier, vol. 263(PA).
    2. Ibrahim Mohamed Diaaeldin & Mahmoud A. Attia & Amr K. Khamees & Othman A. M. Omar & Ahmed O. Badr, 2023. "A Novel Multiobjective Formulation for Optimal Wind Speed Modeling via a Mixture Probability Density Function," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    3. Sajid Ali & Choon-Man Jang, 2019. "Selection of Best-Suited Wind Turbines for New Wind Farm Sites Using Techno-Economic and GIS Analysis in South Korea," Energies, MDPI, vol. 12(16), pages 1-22, August.
    4. Moghadam, Mehdi Akbari & Bagheri, Sajad & Salemi, Amir Hosein & Tavakoli, Mohammad Bagher, 2023. "Long-term maintenance planning of medium voltage overhead lines considering the uncertainties and reasons for interruption in a real distribution network," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Vladimir Simankov & Pavel Buchatskiy & Semen Teploukhov & Stefan Onishchenko & Anatoliy Kazak & Petr Chetyrbok, 2023. "Review of Estimating and Predicting Models of the Wind Energy Amount," Energies, MDPI, vol. 16(16), pages 1-24, August.
    6. Mehr Gul & Nengling Tai & Wentao Huang & Muhammad Haroon Nadeem & Moduo Yu, 2019. "Assessment of Wind Power Potential and Economic Analysis at Hyderabad in Pakistan: Powering to Local Communities Using Wind Power," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    7. Suzer, Ahmet Esat & Atasoy, Vehbi Emrah & Ekici, Selcuk, 2021. "Developing a holistic simulation approach for parametric techno-economic analysis of wind energy," Energy Policy, Elsevier, vol. 149(C).
    8. Rabbani, R. & Zeeshan, M., 2020. "Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan," Renewable Energy, Elsevier, vol. 154(C), pages 1240-1251.
    9. Denis E.K. Dzebre & Muyiwa S. Adaramola, 2019. "Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana," Energies, MDPI, vol. 12(19), pages 1-16, September.
    10. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    11. Krishnamoorthy R & Udhayakumar K & Kannadasan Raju & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2020. "An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization," Energies, MDPI, vol. 13(12), pages 1-41, June.
    12. Ke-Sheng Cheng & Cheng-Yu Ho & Jen-Hsin Teng, 2020. "Wind Characteristics in the Taiwan Strait: A Case Study of the First Offshore Wind Farm in Taiwan," Energies, MDPI, vol. 13(24), pages 1-21, December.

    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. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    2. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
    3. Katinas, Vladislovas & Marčiukaitis, Mantas & Gecevičius, Giedrius & Markevičius, Antanas, 2017. "Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania," Renewable Energy, Elsevier, vol. 113(C), pages 190-201.
    4. Emilio Gómez-Lázaro & María C. Bueso & Mathieu Kessler & Sergio Martín-Martínez & Jie Zhang & Bri-Mathias Hodge & Angel Molina-García, 2016. "Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures," Energies, MDPI, vol. 9(2), pages 1-15, February.
    5. Estefania Artigao & Antonio Vigueras-Rodríguez & Andrés Honrubia-Escribano & Sergio Martín-Martínez & Emilio Gómez-Lázaro, 2021. "Wind Resource and Wind Power Generation Assessment for Education in Engineering," Sustainability, MDPI, vol. 13(5), pages 1-27, February.
    6. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
    7. Wais, Piotr, 2017. "A review of Weibull functions in wind sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1099-1107.
    8. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    9. Mehr Gul & Nengling Tai & Wentao Huang & Muhammad Haroon Nadeem & Moduo Yu, 2019. "Assessment of Wind Power Potential and Economic Analysis at Hyderabad in Pakistan: Powering to Local Communities Using Wind Power," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    10. Usta, Ilhan, 2016. "An innovative estimation method regarding Weibull parameters for wind energy applications," Energy, Elsevier, vol. 106(C), pages 301-314.
    11. Li, Jiale & Wang, Xuefei & Yu, Xiong (Bill), 2018. "Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment," Applied Energy, Elsevier, vol. 213(C), pages 469-485.
    12. Katinas, Vladislovas & Gecevicius, Giedrius & Marciukaitis, Mantas, 2018. "An investigation of wind power density distribution at location with low and high wind speeds using statistical model," Applied Energy, Elsevier, vol. 218(C), pages 442-451.
    13. Suwarno Suwarno & M. Fitra Zambak, 2021. "The Probability Density Function for Wind Speed Using Modified Weibull Distribution," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 544-550.
    14. Arslan, Talha & Bulut, Y. Murat & Altın Yavuz, Arzu, 2014. "Comparative study of numerical methods for determining Weibull parameters for wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 820-825.
    15. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    16. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    17. Akdağ, Seyit Ahmet & Güler, Önder, 2018. "Alternative Moment Method for wind energy potential and turbine energy output estimation," Renewable Energy, Elsevier, vol. 120(C), pages 69-77.
    18. Khahro, Shahnawaz Farhan & Tabbassum, Kavita & Mahmood Soomro, Amir & Liao, Xiaozhong & Alvi, Muhammad Bux & Dong, Lei & Manzoor, M. Farhan, 2014. "Techno-economical evaluation of wind energy potential and analysis of power generation from wind at Gharo, Sindh Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 460-474.
    19. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.
    20. Emeksiz, Cem & Tan, Mustafa, 2022. "Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)," Energy, Elsevier, vol. 249(C).

    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:jeners:v:11:y:2018:i:2:p:356-:d:130085. 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.