IDEAS home Printed from https://ideas.repec.org/a/zib/zbmsmk/v6y2022i1p05-12.html
   My bibliography  Save this article

Multivariate Models For Predicting Global Solar Radiation In Jos, Nigeria

Author

Listed:
  • D.O. Akpootu

    (Department of Physics, Usmanu Danfodiyo University, Sokoto, Nigeria)

  • M. I. Iliyasu

    (Physics Unit, Umaru Ali Shinkafi Polytechnic, Sokoto, Nigeria)

  • B.M. Olomiyesan

    (Examination Development Department, National Examinations Council (NECO))

  • S.A. Fagbemi

    (Department of Physics, Federal University Dutsin-Ma, Katsina, Nigeria)

  • S.B. Sharafa

    (Department of Physics, Usmanu Danfodiyo University, Sokoto, Nigeria)

  • M. Idris

    (Department of Physics, Bayero University, Kano, Nigeria)

  • Z. Abdullahi

    (Department of Physics, Adamu Augie College of Education, Kebbi State, Nigeria)

  • N.O. Meseke

    (Department of Physics, University of Ilorin, Nigeria)

Abstract

This study developed two to six multivariate regression equations that reliably predict global radiation in Jos (Latitude 9.87 Â°ð ‘ and Longitude 8.75 °ð ¸). Thirty-one years (1980 – 2010) observed monthly mean daily global solar radiation, sunshine hours, maximum and minimum temperatures, cloud cover, rainfall, relative humidity and wind speed data were used in this study with the clearness index as the response variable and other variables as predictors. The seven validation indices employed are the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to determine the reliability, suitability and applicability of the developed models. The results in this study revealed that all the developed multivariate models were found reliable for global solar radiation estimation in Jos depending on the obtainable meteorological data measured in the location. The correlation between the measured and predicted (developed) global solar radiation shows a perfect correlation as depicted from the figures.

Suggested Citation

  • D.O. Akpootu & M. I. Iliyasu & B.M. Olomiyesan & S.A. Fagbemi & S.B. Sharafa & M. Idris & Z. Abdullahi & N.O. Meseke, 2022. "Multivariate Models For Predicting Global Solar Radiation In Jos, Nigeria," Matrix Science Mathematic (MSMK), Zibeline International Publishing, vol. 6(1), pages 05-12, July.
    • Handle: RePEc:zib:zbmsmk:v:6:y:2022:i:1:p:05-12
    DOI: 10.26480/msmk.01.2022.05.12
    as

    Download full text from publisher

    File URL: https://matrixsmathematic.com/archives/1msmk2022/1msmk2022-05-12.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.26480/msmk.01.2022.05.12?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
    ---><---

    References listed on IDEAS

    as
    1. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    2. Freitas, S. & Catita, C. & Redweik, P. & Brito, M.C., 2015. "Modelling solar potential in the urban environment: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 915-931.
    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. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    2. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    3. Vrînceanu, Alexandra & Dumitrașcu, Monica & Kucsicsa, Gheorghe, 2022. "Site suitability for photovoltaic farms and current investment in Romania," Renewable Energy, Elsevier, vol. 187(C), pages 320-330.
    4. Formolli, M. & Kleiven, T. & Lobaccaro, G., 2023. "Assessing solar energy accessibility at high latitudes: A systematic review of urban spatial domains, metrics, and parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    5. Zhixin Zhang & Min Chen & Teng Zhong & Rui Zhu & Zhen Qian & Fan Zhang & Yue Yang & Kai Zhang & Paolo Santi & Kaicun Wang & Yingxia Pu & Lixin Tian & Guonian Lü & Jinyue Yan, 2023. "Carbon mitigation potential afforded by rooftop photovoltaic in China," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    6. Finn, Thomas & McKenzie, Paul, 2020. "A high-resolution suitability index for solar farm location in complex landscapes," Renewable Energy, Elsevier, vol. 158(C), pages 520-533.
    7. Fuster-Palop, Enrique & Prades-Gil, Carlos & Masip, X. & Viana-Fons, Joan D. & Payá, Jorge, 2021. "Innovative regression-based methodology to assess the techno-economic performance of photovoltaic installations in urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    8. Hussain, C.M. Iftekhar & Norton, Brian & Duffy, Aidan, 2017. "Technological assessment of different solar-biomass systems for hybrid power generation in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1115-1129.
    9. Mendis, Thushini & Huang, Zhaojian & Xu, Shen & Zhang, Weirong, 2020. "Economic potential analysis of photovoltaic integrated shading strategies on commercial building facades in urban blocks: A case study of Colombo, Sri Lanka," Energy, Elsevier, vol. 194(C).
    10. Lobaccaro, G. & Croce, S. & Lindkvist, C. & Munari Probst, M.C. & Scognamiglio, A. & Dahlberg, J. & Lundgren, M. & Wall, M., 2019. "A cross-country perspective on solar energy in urban planning: Lessons learned from international case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 209-237.
    11. Paulescu, Marius & Badescu, Viorel & Budea, Sanda & Dumitrescu, Alexandru, 2022. "Empirical sunshine-based models vs online estimators for solar resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    12. Hong, Taehoon & Lee, Minhyun & Koo, Choongwan & Jeong, Kwangbok & Kim, Jimin, 2017. "Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis," Applied Energy, Elsevier, vol. 194(C), pages 320-332.
    13. Enrique A. Enríquez-Velásquez & Victor H. Benitez & Sergey G. Obukhov & Luis C. Félix-Herrán & Jorge de-J. Lozoya-Santos, 2020. "Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in Northwestern Territory of Mexico as Case Study," Energies, MDPI, vol. 13(24), pages 1-41, December.
    14. Gonçalves, Juliana E. & Montazeri, Hamid & van Hooff, Twan & Saelens, Dirk, 2021. "Performance of building integrated photovoltaic facades: Impact of exterior convective heat transfer," Applied Energy, Elsevier, vol. 287(C).
    15. Miguel Amado & Francesca Poggi & António Ribeiro Amado & Sílvia Breu, 2018. "E-City Web Platform: A Tool for Energy Efficiency at Urban Level," Energies, MDPI, vol. 11(7), pages 1-14, July.
    16. Chang, Kai & Zhang, Qingyuan, 2019. "Improvement of the hourly global solar model and solar radiation for air-conditioning design in China," Renewable Energy, Elsevier, vol. 138(C), pages 1232-1238.
    17. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    18. Jahani, Babak & Dinpashoh, Y. & Raisi Nafchi, Atefeh, 2017. "Evaluation and development of empirical models for estimating daily solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 878-891.
    19. Qin, Wenmin & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Xia, Xiangao & Hu, Bo & Niu, Zigeng, 2018. "Comparison of deterministic and data-driven models for solar radiation estimation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 579-594.
    20. Matteo Formolli & Gabriele Lobaccaro & Jouri Kanters, 2021. "Solar Energy in the Nordic Built Environment: Challenges, Opportunities and Barriers," Energies, MDPI, vol. 14(24), pages 1-18, December.

    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:zib:zbmsmk:v:6:y:2022:i:1:p:05-12. 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: Zibeline International Publishing (email available below). General contact details of provider: https://matrixsmathematic.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.