IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v86y2009i9p1410-1422.html
   My bibliography  Save this article

Modelling of solar energy potential in Nigeria using an artificial neural network model

Author

Listed:
  • Fadare, D.A.

Abstract

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14°N, log. 2-15°E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:86:y:2009:i:9:p:1410-1422
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(08)00323-1
    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. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    2. 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.
    3. 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.
    4. Kalogirou, Soteris A. & Neocleous, Constantinos C. & Schizas, Christos N., 1998. "Artificial neural networks for modelling the starting-up of a solar steam-generator," Applied Energy, Elsevier, vol. 60(2), pages 89-100, June.
    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. Kashyap, Yashwant & Bansal, Ankit & Sao, Anil K., 2015. "Solar radiation forecasting with multiple parameters neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 825-835.
    2. 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).
    3. Janjai, S. & Pankaew, P. & Laksanaboonsong, J., 2009. "A model for calculating hourly global solar radiation from satellite data in the tropics," Applied Energy, Elsevier, vol. 86(9), pages 1450-1457, September.
    4. 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.
    5. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    6. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    7. Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C.G., 2008. "Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques," Renewable Energy, Elsevier, vol. 33(8), pages 1796-1803.
    8. Long, Huan & Zhang, Zijun & Su, Yan, 2014. "Analysis of daily solar power prediction with data-driven approaches," Applied Energy, Elsevier, vol. 126(C), pages 29-37.
    9. 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.
    10. 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.
    11. Wang, Guochang & Su, Yan & Shu, Lianjie, 2016. "One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models," Renewable Energy, Elsevier, vol. 96(PA), pages 469-478.
    12. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    13. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2012. "Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation," Energy, Elsevier, vol. 39(1), pages 341-355.
    14. Şenol, Halil & Ali Dereli̇, Mehmet & Özbilgin, Ferdi, 2021. "Investigation of the distribution of bovine manure-based biomethane potential using an artificial neural network in Turkey to 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    15. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    16. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
    17. Voyant, Cyril & Paoli, Christophe & Muselli, Marc & Nivet, Marie-Laure, 2013. "Multi-horizon solar radiation forecasting for Mediterranean locations using time series models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 44-52.
    18. Purohit, Ishan & Purohit, Pallav, 2015. "Inter-comparability of solar radiation databases in Indian context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 735-747.
    19. Hocaoglu, Fatih Onur & Karanfil, Fatih, 2013. "A time series-based approach for renewable energy modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 204-214.
    20. Dahmani, Kahina & Notton, Gilles & Voyant, Cyril & Dizene, Rabah & Nivet, Marie Laure & Paoli, Christophe & Tamas, Wani, 2016. "Multilayer Perceptron approach for estimating 5-min and hourly horizontal global irradiation from exogenous meteorological data in locations without solar measurements," Renewable Energy, Elsevier, vol. 90(C), pages 267-282.

    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:86:y:2009:i:9:p:1410-1422. 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.