IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v101y2013icp582-588.html
   My bibliography  Save this article

Generalized feed-forward based method for wind energy prediction

Author

Listed:
  • Celik, Ali N.
  • Kolhe, Mohan

Abstract

Even though a number of new mathematical functions have been proposed for modeling wind speed probability density distributions, still the Weibull function continues to be the most commonly used model in the literature. Therefore, the parameters of this function are still widely used to obtain typical wind probability density distributions for finding the wind energy potential by researchers, engineers and designers. Once long-term average of Weibull function’s parameters are known, then the probability density distributions can easily be obtained. Artificial neural network (ANN) can be used as alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems. In this work, a generalized feed-forward type of neural network is used to predict an annual wind speed probability density distribution by using the Weibull function’s parameters as inputs. For verifying its validity and merits, the annual wind speed probability density distribution is also predicted by using the Weibull function. The wind speed distribution predicted from the ANN modeling is compared with the analytical model’s results. Total 9year long hourly wind speed data, belonging to one of the windiest locations in Turkey with mean wind speed of over 6m/s, are used in this study. It is observed that ANN based wind speed distribution estimation gives better results for calculating the energy output from some commercial wind turbine generators.

Suggested Citation

  • Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
    • Handle: RePEc:eee:appene:v:101:y:2013:i:c:p:582-588
    DOI: 10.1016/j.apenergy.2012.06.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261912004850
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2012.06.040?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. Feretić, Danilo & Tomšić, Željko & Čavlina, Nikola, 1999. "Feasibility analysis of wind-energy utilization in Croatia," Energy, Elsevier, vol. 24(3), pages 239-246.
    2. Mathew, Sathyajith & Pandey, K.P. & Kumar.V, Anil, 2002. "Analysis of wind regimes for energy estimation," Renewable Energy, Elsevier, vol. 25(3), pages 381-399.
    3. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
    4. Fadare, D.A., 2010. "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, Elsevier, vol. 87(3), pages 934-942, March.
    5. 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.
    6. Safari, Bonfils & Gasore, Jimmy, 2010. "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda," Renewable Energy, Elsevier, vol. 35(12), pages 2874-2880.
    7. 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.
    8. Karayiannis, Nicolaos B., 1996. "Hybrid learning schemes for fast training of feed-forward neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 41(1), pages 13-28.
    9. 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.
    10. Ouammi, Ahmed & Dagdougui, Hanane & Sacile, Roberto & Mimet, Abdelaziz, 2010. "Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1959-1968, September.
    11. Thiaw, L. & Sow, G. & Fall, S.S. & Kasse, M. & Sylla, E. & Thioye, S., 2010. "A neural network based approach for wind resource and wind generators production assessment," Applied Energy, Elsevier, vol. 87(5), pages 1744-1748, May.
    12. Gualtieri, Giovanni & Secci, Sauro, 2011. "Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy," Renewable Energy, Elsevier, vol. 36(3), pages 1081-1094.
    13. Morgan, Vincent T., 1995. "Statistical distributions of wind parameters at Sydney, Australia," Renewable Energy, Elsevier, vol. 6(1), pages 39-47.
    14. 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. González-Alonso de Linaje, N. & Mattar, C. & Borvarán, D., 2019. "Quantifying the wind energy potential differences using different WRF initial conditions on Mediterranean coast of Chile," Energy, Elsevier, vol. 188(C).
    2. Zhang, Hua & Yu, Yong-Jing & Liu, Zhi-Yuan, 2014. "Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea," Applied Energy, Elsevier, vol. 114(C), pages 931-938.
    3. Wu, Xuedong & Zhu, Zhiyu & Su, Xunliang & Fan, Shaosheng & Du, Zhaoping & Chang, Yanchao & Zeng, Qingjun, 2015. "A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction," Energy, Elsevier, vol. 88(C), pages 194-201.
    4. Hao, Yan & Tian, Chengshi, 2019. "A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 238(C), pages 368-383.
    5. Putz, Dominik & Gumhalter, Michael & Auer, Hans, 2021. "A novel approach to multi-horizon wind power forecasting based on deep neural architecture," Renewable Energy, Elsevier, vol. 178(C), pages 494-505.
    6. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    7. Ciprian Sorandaru & Sorin Musuroi & Flaviu Mihai Frigura-Iliasa & Doru Vatau & Marian Dordescu, 2019. "Analysis of the Wind System Operation in the Optimal Energetic Area at Variable Wind Speed over Time," Sustainability, MDPI, vol. 11(5), pages 1-16, February.
    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. Deep, Sneh & Sarkar, Arnab & Ghawat, Mayur & Rajak, Manoj Kumar, 2020. "Estimation of the wind energy potential for coastal locations in India using the Weibull model," Renewable Energy, Elsevier, vol. 161(C), pages 319-339.
    10. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    11. Matthew E. Wilhelm & Chenyu Wang & Matthew D. Stuber, 2023. "Convex and concave envelopes of artificial neural network activation functions for deterministic global optimization," Journal of Global Optimization, Springer, vol. 85(3), pages 569-594, March.
    12. Cristian Mattar & Felipe Cabello-Españon & Nicolas G. Alonso-de-Linaje, 2021. "Towards a Future Scenario for Offshore Wind Energy in Chile: Breaking the Paradigm," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    13. Drisya, G.V. & Asokan, K. & Kumar, K. Satheesh, 2018. "Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations," Renewable Energy, Elsevier, vol. 119(C), pages 540-550.
    14. 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.
    15. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    16. Christopher Jung, 2016. "High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series," Energies, MDPI, vol. 9(5), pages 1-20, May.
    17. 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.
    18. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    19. Lydia, M. & Suresh Kumar, S. & Immanuel Selvakumar, A. & Edwin Prem Kumar, G., 2015. "Wind resource estimation using wind speed and power curve models," Renewable Energy, Elsevier, vol. 83(C), pages 425-434.
    20. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    21. 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. El Alimi, Souheil & Maatallah, Taher & Dahmouni, Anouar Wajdi & Ben Nasrallah, Sassi, 2012. "Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5466-5478.
    2. 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.
    3. Usta, Ilhan, 2016. "An innovative estimation method regarding Weibull parameters for wind energy applications," Energy, Elsevier, vol. 106(C), pages 301-314.
    4. Bahrami, Arian & Teimourian, Amir & Okoye, Chiemeka Onyeka & Khosravi, Nima, 2019. "Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market," Energy, Elsevier, vol. 183(C), pages 415-427.
    5. 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.
    6. Belabes, B. & Youcefi, A. & Guerri, O. & Djamai, M. & Kaabeche, A., 2015. "Evaluation of wind energy potential and estimation of cost using wind energy turbines for electricity generation in north of Algeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1245-1255.
    7. 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.
    8. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    9. 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.
    10. Sergei Kolesnik & Yossi Rabinovitz & Michael Byalsky & Asher Yahalom & Alon Kuperman, 2023. "Assessment of Wind Speed Statistics in Samaria Region and Potential Energy Production," Energies, MDPI, vol. 16(9), pages 1-35, May.
    11. Liu, Feng-Jiao & Chen, Pai-Hsun & Kuo, Shyi-Shiun & Su, De-Chuan & Chang, Tian-Pau & Yu, Yu-Hua & Lin, Tsung-Chi, 2011. "Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait," Energy, Elsevier, vol. 36(5), pages 2611-2619.
    12. 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.
    13. Ohunakin, O.S. & Adaramola, M.S. & Oyewola, O.M., 2011. "Wind energy evaluation for electricity generation using WECS in seven selected locations in Nigeria," Applied Energy, Elsevier, vol. 88(9), pages 3197-3206.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Liu, Feng Jiao & Chang, Tian Pau, 2011. "Validity analysis of maximum entropy distribution based on different moment constraints for wind energy assessment," Energy, Elsevier, vol. 36(3), pages 1820-1826.
    19. Oluseyi O. Ajayi & Richard O. Fagbenle & James Katende & Julius M. Ndambuki & David O. Omole & Adekunle A. Badejo, 2014. "Wind Energy Study and Energy Cost of Wind Electricity Generation in Nigeria: Past and Recent Results and a Case Study for South West Nigeria," Energies, MDPI, vol. 7(12), pages 1-27, December.
    20. 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.

    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:appene:v:101:y:2013:i:c:p:582-588. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.