IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1515-d1056651.html
   My bibliography  Save this article

Comparative Analysis of Eight Numerical Methods Using Weibull Distribution to Estimate Wind Power Density for Coastal Areas in Pakistan

Author

Listed:
  • Iqrar Hussain

    (Dipartimento di Ingegneria Elettrica e dell’Informazione “M. Scarano”, Università di Cassino e del LM, Via G. Di Biasio 43, 03043 Cassino, FR, Italy
    Department of Electrical Engineering, University of Management and Technology Lahore, Sialkot Campus, Sialkot 51310, Pakistan)

  • Aun Haider

    (Department of Electrical Engineering, University of Management and Technology Lahore, Sialkot Campus, Sialkot 51310, Pakistan)

  • Zahid Ullah

    (Department of Electrical Engineering, University of Management and Technology Lahore, Sialkot Campus, Sialkot 51310, Pakistan
    Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, MI, Italy)

  • Mario Russo

    (Dipartimento di Ingegneria Elettrica e dell’Informazione “M. Scarano”, Università di Cassino e del LM, Via G. Di Biasio 43, 03043 Cassino, FR, Italy)

  • Giovanni Mercurio Casolino

    (Dipartimento di Ingegneria Elettrica e dell’Informazione “M. Scarano”, Università di Cassino e del LM, Via G. Di Biasio 43, 03043 Cassino, FR, Italy)

  • Babar Azeem

    (Department of Electrical Energy and Mobility System, Carinthia University of Applied Sciences, 9524 Villach, Austria)

Abstract

Currently, Pakistan is facing severe energy crises and global warming effects. Hence, there is an urgent need to utilize renewable energy generation. In this context, Pakistan possesses massive wind energy potential across the coastal areas. This paper investigates and numerically analyzes coastal areas’ wind power density potential. Eight different state-of-the-art numerical methods, namely an (a) empirical method, (b) graphical method, (c) wasp algorithm, (d) energy pattern method, (e) moment method, (f) maximum likelihood method, (g) energy trend method, and (h) least-squares regression method, were analyzed to calculate Weibull parameters. We computed Weibull shape parameters (WSP) and Weibull scale parameters (WCP) for four regions: Jiwani, Gwadar, Pasni, and Ormara in Pakistan. These Weibull parameters from the above-mentioned numerical methods were analyzed and compared to find an optimal numerical method for the coastal areas of Pakistan. Further, the following statistical indicators were used to compare the efficiency of the above numerical methods: (i) analysis of variance ( R 2 ), (ii) chi-square ( X 2 ), and (iii) root mean square error (RMSE). The performance validation showed that the energy trend and graphical method provided weak performance for the observed period for four coastal regions of Pakistan. Further, we observed that Ormara is the best and Jiwani is the worst area for wind power generation using comparative analyses for actual and estimated data of wind power density from four regions of Pakistan.

Suggested Citation

  • Iqrar Hussain & Aun Haider & Zahid Ullah & Mario Russo & Giovanni Mercurio Casolino & Babar Azeem, 2023. "Comparative Analysis of Eight Numerical Methods Using Weibull Distribution to Estimate Wind Power Density for Coastal Areas in Pakistan," Energies, MDPI, vol. 16(3), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1515-:d:1056651
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1515/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1515/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mazhar H. Baloch & Safdar A. Abro & Ghulam Sarwar Kaloi & Nayyar H. Mirjat & Sohaib Tahir & M. Haroon Nadeem & Mehr Gul & Zubair A. Memon & Mahendar Kumar, 2017. "A Research on Electricity Generation from Wind Corridors of Pakistan (Two Provinces): A Technical Proposal for Remote Zones," Sustainability, MDPI, vol. 9(9), pages 1-31, September.
    2. Kaleem Ullah & Abdul Basit & Zahid Ullah & Rafiq Asghar & Sheraz Aslam & Ayman Yafoz, 2022. "Line Overload Alleviations in Wind Energy Integrated Power Systems Using Automatic Generation Control," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    3. Sahu, Bikash Kumar, 2018. "Wind energy developments and policies in China: A short review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1393-1405.
    4. Hanifa Teimourian & Mahmoud Abubakar & Melih Yildiz & Amir Teimourian, 2022. "A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution," Energies, MDPI, vol. 15(15), pages 1-15, August.
    5. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    6. Baseer, M.A. & Meyer, J.P. & Rehman, S. & Alam, Md. Mahbub, 2017. "Wind power characteristics of seven data collection sites in Jubail, Saudi Arabia using Weibull parameters," Renewable Energy, Elsevier, vol. 102(PA), pages 35-49.
    7. Sumair, Muhammad & Aized, Tauseef & Aslam Bhutta, Muhammad Mahmood & Siddiqui, Farrukh Arsalan & Tehreem, Layba & Chaudhry, Abduallah, 2022. "Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications," Renewable Energy, Elsevier, vol. 191(C), pages 291-304.
    8. Hussain, I. & Ali, S.M. & Khan, B. & Ullah, Z. & Mehmood, C.A. & Jawad, M. & Farid, U. & Haider, A., 2019. "Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers," Renewable Energy, Elsevier, vol. 134(C), pages 1017-1033.
    9. Rafiq Asghar & Zahid Ullah & Babar Azeem & Sheraz Aslam & Muhammad Harris Hashmi & Ehtsham Rasool & Bilawal Shaker & Muhammad Junaid Anwar & Kainat Mustafa, 2022. "Wind Energy Potential in Pakistan: A Feasibility Study in Sindh Province," Energies, MDPI, vol. 15(22), pages 1-23, November.
    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. 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.
    2. Hamza S. Abdalla Lagili & Aşkın Kiraz & Youssef Kassem & Hüseyin Gökçekuş, 2023. "Wind and Solar Energy for Sustainable Energy Production for Family Farms in Coastal Agricultural Regions of Libya Using Measured and Multiple Satellite Datasets," Energies, MDPI, vol. 16(18), pages 1-53, September.

    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. 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.
    2. Kaleem Ullah & Zahid Ullah & Sheraz Aslam & Muhammad Salik Salam & Muhammad Asjad Salahuddin & Muhammad Farooq Umer & Mujtaba Humayon & Haris Shaheer, 2023. "Wind Farms and Flexible Loads Contribution in Automatic Generation Control: An Extensive Review and Simulation," Energies, MDPI, vol. 16(14), pages 1-34, July.
    3. Jianxing Yu & Yiqin Fu & Yang Yu & Shibo Wu & Yuanda Wu & Minjie You & Shuai Guo & Mu Li, 2019. "Assessment of Offshore Wind Characteristics and Wind Energy Potential in Bohai Bay, China," Energies, MDPI, vol. 12(15), pages 1-19, July.
    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. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, December.
    6. 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.
    7. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    8. Mazhar Hussain Baloch & Dahaman Ishak & Sohaib Tahir Chaudary & Baqir Ali & Ali Asghar Memon & Touqeer Ahmed Jumani, 2019. "Wind Power Integration: An Experimental Investigation for Powering Local Communities," Energies, MDPI, vol. 12(4), pages 1-24, February.
    9. Bu, Yan & Wang, Erda & Möst, Dominik & Lieberwirth, Martin, 2022. "How population migration affects carbon emissions in China: Factual and counterfactual scenario analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    10. Wang, Yongli & Li, Jiapu & Wang, Shuo & Yang, Jiale & Qi, Chengyuan & Guo, Hongzhen & Liu, Ximei & Zhang, Hongqing, 2020. "Operational optimization of wastewater reuse integrated energy system," Energy, Elsevier, vol. 200(C).
    11. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    12. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(C).
    13. Rodriguez-Hernandez, O. & Jaramillo, O.A. & Andaverde, J.A. & del Río, J.A., 2013. "Analysis about sampling, uncertainties and selection of a reliable probabilistic model of wind speed data used on resource assessment," Renewable Energy, Elsevier, vol. 50(C), pages 244-252.
    14. Abdulrashid Muhammad Kabir & Mohsin Kamal & Fiaz Ahmad & Zahid Ullah & Fahad R. Albogamy & Ghulam Hafeez & Faizan Mehmood, 2021. "Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    15. Jijian Lian & Yue Zhao & Chong Lian & Haijun Wang & Xiaofeng Dong & Qi Jiang & Huan Zhou & Junni Jiang, 2018. "Application of an Eddy Current-Tuned Mass Damper to Vibration Mitigation of Offshore Wind Turbines," Energies, MDPI, vol. 11(12), pages 1-18, November.
    16. Wang, Han & Yan, Jie & Han, Shuang & Liu, Yongqian, 2020. "Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVs," Renewable Energy, Elsevier, vol. 157(C), pages 256-272.
    17. Andrés Ruiz & Florin Onea & Eugen Rusu, 2020. "Study Concerning the Expected Dynamics of the Wind Energy Resources in the Iberian Nearshore," Energies, MDPI, vol. 13(18), pages 1-25, September.
    18. 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.
    19. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    20. Chang, Tian-Pau & Ko, Hong-Hsi & Liu, Feng-Jiao & Chen, Pai-Hsun & Chang, Ying-Pin & Liang, Ying-Hsin & Jang, Horng-Yuan & Lin, Tsung-Chi & Chen, Yi-Hwa, 2012. "Fractal dimension of wind speed time series," Applied Energy, Elsevier, vol. 93(C), pages 742-749.

    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:16:y:2023:i:3:p:1515-:d:1056651. 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.