IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v115y2018icp1260-1280.html
   My bibliography  Save this article

Estimation of wind speed probability density function using a mixture of two truncated normal distributions

Author

Listed:
  • Mazzeo, Domenico
  • Oliveti, Giuseppe
  • Labonia, Ester

Abstract

Probability density functions (PDFs) are normally used to describe wind speed distribution for the proper selection of wind turbines in a given location. The identification of a suitable PDF is fundamental for accurately assessing the wind energy potential and designing the wind farms.

Suggested Citation

  • Mazzeo, Domenico & Oliveti, Giuseppe & Labonia, Ester, 2018. "Estimation of wind speed probability density function using a mixture of two truncated normal distributions," Renewable Energy, Elsevier, vol. 115(C), pages 1260-1280.
  • Handle: RePEc:eee:renene:v:115:y:2018:i:c:p:1260-1280
    DOI: 10.1016/j.renene.2017.09.043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148117309059
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2017.09.043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Wu, Jie & Wang, Jianzhou & Chi, Dezhong, 2013. "Wind energy potential assessment for the site of Inner Mongolia in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 215-228.
    2. Jaramillo, O.A & Saldaña, R & Miranda, U, 2004. "Wind power potential of Baja California Sur, México," Renewable Energy, Elsevier, vol. 29(13), pages 2087-2100.
    3. Celik, Ali Naci, 2004. "A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey," Renewable Energy, Elsevier, vol. 29(4), pages 593-604.
    4. Shin, Ju-Young & Ouarda, Taha B.M.J. & Lee, Taesam, 2016. "Heterogeneous mixture distributions for modeling wind speed, application to the UAE," Renewable Energy, Elsevier, vol. 91(C), pages 40-52.
    5. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    6. Dabbaghiyan, Amir & Fazelpour, Farivar & Abnavi, Mohhamadreza Dehghan & Rosen, Marc A., 2016. "Evaluation of wind energy potential in province of Bushehr, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 455-466.
    7. Kainkwa, R.M.R., 2000. "Wind speed pattern and the available wind power at Basotu, Tanzania," Renewable Energy, Elsevier, vol. 21(2), pages 289-295.
    8. Hossain, Jami & Sharma, Suman & Kishore, V.V.N., 2014. "Multi-peak Gaussian fit applicability to wind speed distribution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 483-490.
    9. 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.
    10. 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.
    11. Pallabazzer, Rodolfo, 2004. "Previsional estimation of the energy output of windgenerators," Renewable Energy, Elsevier, vol. 29(3), pages 413-420.
    12. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    13. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
    14. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
    15. Lo Brano, Valerio & Orioli, Aldo & Ciulla, Giuseppina & Culotta, Simona, 2011. "Quality of wind speed fitting distributions for the urban area of Palermo, Italy," Renewable Energy, Elsevier, vol. 36(3), pages 1026-1039.
    16. Bataineh, Khaled M. & Dalalah, Doraid, 2013. "Assessment of wind energy potential for selected areas in Jordan," Renewable Energy, Elsevier, vol. 59(C), pages 75-81.
    17. Soukissian, Takvor, 2013. "Use of multi-parameter distributions for offshore wind speed modeling: The Johnson SB distribution," Applied Energy, Elsevier, vol. 111(C), pages 982-1000.
    18. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    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. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
    2. Kalim Ullah & Sajjad Ali & Taimoor Ahmad Khan & Imran Khan & Sadaqat Jan & Ibrar Ali Shah & Ghulam Hafeez, 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs," Energies, MDPI, vol. 13(21), pages 1-17, November.
    3. Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.
    4. Hiba H. Darwish & Ayman Al-Quraan, 2023. "Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    5. Ban, Guihua & Chen, Yan & Xiong, Zhenhua & Zhuo, Yixin & Huang, Kui, 2024. "The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer," Energy, Elsevier, vol. 290(C).
    6. Yinan Li & Kai-Tai Fang & Ping He & Heng Peng, 2022. "Representative Points from a Mixture of Two Normal Distributions," Mathematics, MDPI, vol. 10(21), pages 1-28, October.
    7. Bagci, Kubra & Arslan, Talha & Celik, H. Eray, 2021. "Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. 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.
    9. Pan, Yue & Qin, Jianjun, 2022. "A novel probabilistic modeling framework for wind speed with highlight of extremes under data discrepancy and uncertainty," Applied Energy, Elsevier, vol. 326(C).
    10. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    11. Kwami Senam A. Sedzro & Adekunlé Akim Salami & Pierre Akuété Agbessi & Mawugno Koffi Kodjo, 2022. "Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)," Energies, MDPI, vol. 15(22), pages 1-28, November.

    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. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. 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.
    3. 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.
    4. Alrashidi, Musaed & Rahman, Saifur & Pipattanasomporn, Manisa, 2020. "Metaheuristic optimization algorithms to estimate statistical distribution parameters for characterizing wind speeds," Renewable Energy, Elsevier, vol. 149(C), pages 664-681.
    5. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
    6. Shin, Ju-Young & Ouarda, Taha B.M.J. & Lee, Taesam, 2016. "Heterogeneous mixture distributions for modeling wind speed, application to the UAE," Renewable Energy, Elsevier, vol. 91(C), pages 40-52.
    7. Bagci, Kubra & Arslan, Talha & Celik, H. Eray, 2021. "Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. 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.
    9. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
    10. Kantar, Yeliz Mert & Usta, Ilhan & Arik, Ibrahim & Yenilmez, Ismail, 2018. "Wind speed analysis using the Extended Generalized Lindley Distribution," Renewable Energy, Elsevier, vol. 118(C), pages 1024-1030.
    11. Collados-Lara, Antonio-Juan & Baena-Ruiz, Leticia & Pulido-Velazquez, David & Pardo-Igúzquiza, Eulogio, 2022. "Data-driven mapping of hourly wind speed and its potential energy resources: A sensitivity analysis," Renewable Energy, Elsevier, vol. 199(C), pages 87-102.
    12. Masseran, Nurulkamal, 2015. "Evaluating wind power density models and their statistical properties," Energy, Elsevier, vol. 84(C), pages 533-541.
    13. Jia, Junmei & Yan, Zaizai & Peng, Xiuyun & An, Xiaoyan, 2020. "A new distribution for modeling the wind speed data in Inner Mongolia of China," Renewable Energy, Elsevier, vol. 162(C), pages 1979-1991.
    14. Chadee, Xsitaaz T. & Clarke, Ricardo M., 2018. "Wind resources and the levelized cost of wind generated electricity in the Caribbean islands of Trinidad and Tobago," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2526-2540.
    15. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    16. Siyavash Filom & Soheil Radfar & Roozbeh Panahi & Erfan Amini & Mehdi Neshat, 2021. "Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    17. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
    18. Mónica Borunda & Katya Rodríguez-Vázquez & Raul Garduno-Ramirez & Javier de la Cruz-Soto & Javier Antunez-Estrada & Oscar A. Jaramillo, 2020. "Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming," Energies, MDPI, vol. 13(8), pages 1-24, April.
    19. 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.
    20. Usta, Ilhan, 2016. "An innovative estimation method regarding Weibull parameters for wind energy applications," Energy, Elsevier, vol. 106(C), pages 301-314.

    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:renene:v:115:y:2018:i:c:p:1260-1280. 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.journals.elsevier.com/renewable-energy .

    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.