IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v87y2010i3p934-942.html
   My bibliography  Save this article

The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria

Author

Listed:
  • Fadare, D.A.

Abstract

Modelling and prediction of wind speed are essential prerequisites in the sitting and sizing of wind power applications. The profile of wind speed in Nigeria is modelled using artificial neural network (ANN). The ANN model consists of 3-layered, feed-forward, back-propagation network with different configurations, designed using the Neural Toolbox for MATLAB. The monthly mean daily wind speed data monitored at 10Â m above ground level for a period of 20Â years (1983-2003) for 28 ground stations operated by the Nigeria Meteorological Services (NIMET) were used as training (18 stations) and testing (10 stations) dataset. The geographical parameters (latitude, longitude and altitude) and the month of the year were used as input data, while the monthly mean wind speed was used as the output of the network. The optimum network architecture with minimum Mean Absolute Percentage Error (MAPE) of 8.9% and correlation coefficient (r) between the predicted and the measured wind speed values of 0.9380 was obtained. The predicted monthly wind speed ranged from 0.9-13.1Â m/s with an annual mean of 4.7Â m/s. The model predicted wind speed values are given in the form of monthly maps, which can be easily used for assessment of wind energy potential for different locations within Nigeria.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:3:p:934-942
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(09)00381-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
    2. Xydis, G. & Koroneos, C. & Loizidou, M., 2009. "Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece," Applied Energy, Elsevier, vol. 86(11), pages 2411-2420, November.
    3. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis," Renewable Energy, Elsevier, vol. 33(6), pages 1237-1250.
    4. Ngala, G.M. & Alkali, B. & Aji, M.A., 2007. "Viability of wind energy as a power generation source in Maiduguri, Borno state, Nigeria," Renewable Energy, Elsevier, vol. 32(13), pages 2242-2246.
    5. John-Felix Akinbami, 2001. "Renewable energy resources and technologies in Nigeria: present situation, future prospects and policy framework," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 6(2), pages 155-182, June.
    6. 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.
    7. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    8. Kaldellis, J.K. & Kavadias, K.A. & Filios, A.E., 2009. "A new computational algorithm for the calculation of maximum wind energy penetration in autonomous electrical generation systems," Applied Energy, Elsevier, vol. 86(7-8), pages 1011-1023, July.
    9. Bekele, Getachew & Palm, Björn, 2009. "Wind energy potential assessment at four typical locations in Ethiopia," Applied Energy, Elsevier, vol. 86(3), pages 388-396, March.
    10. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    11. Mayhoub, A.B. & Azzam, A., 1997. "A survey on the assessment of wind energy potential in Egypt," Renewable Energy, Elsevier, vol. 11(2), pages 235-247.
    12. Weigt, Hannes, 2009. "Germany's wind energy: The potential for fossil capacity replacement and cost saving," Applied Energy, Elsevier, vol. 86(10), pages 1857-1863, October.
    13. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: A neural kriging application," Renewable Energy, Elsevier, vol. 33(6), pages 1251-1266.
    14. Adekoya, L.O. & Adewale, A.A., 1992. "Wind energy potential of Nigeria," Renewable Energy, Elsevier, vol. 2(1), pages 35-39.
    15. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    16. Riahy, G.H. & Abedi, M., 2008. "Short term wind speed forecasting for wind turbine applications using linear prediction method," Renewable Energy, Elsevier, vol. 33(1), pages 35-41.
    17. Mohandes, M. & Rehman, S. & Halawani, T.O., 1998. "Estimation of global solar radiation using artificial neural networks," Renewable Energy, Elsevier, vol. 14(1), pages 179-184.
    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. 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.
    2. Ouammi, Ahmed & Zejli, Driss & Dagdougui, Hanane & Benchrifa, Rachid, 2012. "Artificial neural network analysis of Moroccan solar potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4876-4889.
    3. Heo, Jae & Jung, Jaehoon & Kim, Byungil & Han, SangUk, 2020. "Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions," Applied Energy, Elsevier, vol. 262(C).
    4. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    5. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
    6. Jabar H. Yousif & Hussein A. Kazem & John Boland, 2017. "Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions," Energies, MDPI, vol. 10(7), pages 1-19, July.
    7. Calif, Rudy & Emilion, Richard & Soubdhan, Ted, 2011. "Classification of wind speed distributions using a mixture of Dirichlet distributions," Renewable Energy, Elsevier, vol. 36(11), pages 3091-3097.
    8. Ugwoke, B. & Gershon, O. & Becchio, C. & Corgnati, S.P. & Leone, P., 2020. "A review of Nigerian energy access studies: The story told so far," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    9. Akdag, Seyit Ahmet & Güler, Önder, 2010. "Evaluation of wind energy investment interest and electricity generation cost analysis for Turkey," Applied Energy, Elsevier, vol. 87(8), pages 2574-2580, August.
    10. Xydis, G. & Koroneos, C. & Loizidou, M., 2009. "Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece," Applied Energy, Elsevier, vol. 86(11), pages 2411-2420, November.
    11. Khalil, Samy A. & Shaffie, A.M., 2016. "Evaluation of transposition models of solar irradiance over Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 105-119.
    12. Mostafaeipour, Ali & Jadidi, Mohsen & Mohammadi, Kasra & Sedaghat, Ahmad, 2014. "An analysis of wind energy potential and economic evaluation in Zahedan, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 641-650.
    13. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    14. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    15. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    16. Sözen, Adnan & Arcaklioglu, Erol & Özkaymak, Mehmet, 2005. "Turkey's net energy consumption," Applied Energy, Elsevier, vol. 81(2), pages 209-221, June.
    17. Mubiru, J., 2008. "Predicting total solar irradiation values using artificial neural networks," Renewable Energy, Elsevier, vol. 33(10), pages 2329-2332.
    18. Shaaban, Mohamed & Petinrin, J.O., 2014. "Renewable energy potentials in Nigeria: Meeting rural energy needs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 72-84.
    19. González-Longatt, Francisco & Medina, Humberto & Serrano González, Javier, 2015. "Spatial interpolation and orographic correction to estimate wind energy resource in Venezuela," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 1-16.
    20. Sözen, Adnan & Ali Akçayol, M., 2004. "Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle," Applied Energy, Elsevier, vol. 79(3), pages 309-325, November.

    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:87:y:2010:i:3:p:934-942. 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.