Comparative Study of Multiple Regression Model with Curvefit Model for The Prediction of Solar Radiation in Mubi Town Adamawa State, Nigeria

Main Article Content

Luqman Raji
Maxwell Francis
A K Issa

Keywords

Solar radiation, Curvefit model, Air temperature, Relative humidity, Mubi town and NASA

Abstract

The sun emits solar radiation, which is critical for researchers working on renewable energy technology that provides ecologically favorable power systems. This research created a new model to forecast DHSR for the Mubi metropolitan area in Adamawa State, Nigeria. Data for this study were obtained from the National Aeronautics and Space Administration (NASA) over a 22-year period (2000 – 2021). When DHSR was employed as an output, the requested values were air temperature (Tai) and relative humidity (Rhi). The MATHLAB curve fitting program was used to create the new DHSR mathematical model. The model was validated using five statistical methods in this study: MSE (mean square error); SSE (sum of square errors); RMSE, Chi-square error (X2), and the absolute fraction of variance (R2) are 0.0005, 0.0064, 0.0231, 0.0011kWh/m2/day, and 0.9998, respectively.

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References

[1] Y. A. Cengel, Thermodynamics: An Engineering Approach, 8th ed. New York: McGraw-Hill Education, 2004.
[2] F. S. Johnson, “The solar constant,” J. Atmos. Sci., vol. 11, pp. 431–439, 1954, doi: 10.1175/1520-0469(1954)011<0431:TSC>2.0.CO;2.
[3] A. Shahsavari and M. Akbari, “Potential of solar energy in developing countries for reducing energy-related emissions,” Renew. Sustain. Energy Rev., vol. 90, pp. 275–291, 2018, doi: 10.1016/j.rser.2018.03.065.
[4] H. Soonmin, A. Lomi, E. C. Okoroigwe, and L. R. Urrego, “Investigation of solar energy: The case study in Malaysia, Indonesia, Colombia and Nigeria,” Int. J. Renew. Energy Res., vol. 9, no. 1, pp. 86–95, 2019, doi: 10.20508/ijrer.v9i1.8699.g7620.
[5] H. Z. Al Garni and A. Awasthi, “Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia,” Appl. Energy, vol. 206, pp. 1225–1240, 2017, doi: 10.1016/j.apenergy.2017.10.024.
[6] L. Raji, R. O. Amusat, O. J. Anjorin, M. H. Idris, and A. K. Issa, “Predictions of Daily Horizontal Solar Radiation for Rural Development: The Case of Mubi Town, Adamawa State, Nigeria,” Curr. J. Int. J. Appl. Technol. Res., vol. 1, no. 1, pp. 38–44, 2020, doi: 10.35313/ijatr.v1i1.22.
[7] O. Ogunmodimu and E. C. Okoroigwe, “Concentrating solar power technologies for solar thermal grid electricity in Nigeria: A review,” Renew. Sustain. Energy Rev., vol. 90, pp. 104–119, 2018, doi: 10.1016/j.rser.2018.03.029.
[8] O. S. Ohunakin, M. S. Adaramola, O. M. Oyewola, and R. O. Fagbenle, “Solar energy applications and development in Nigeria: Drivers and barriers,” Renew. Sustain. Energy Rev., vol. 32, pp. 294–301, 2014, doi: 10.1016/j.rser.2014.01.014.
[9] A. B. Owolabi, B. E. K. Nsafon, and J. S. Huh, “Validating the techno-economic and environmental sustainability of solar PV technology in Nigeria using RETScreen Experts to assess its viability,” Sustain. Energy Technol. Assessments, vol. 36, 2019, doi: 10.1016/j.seta.2019.100542.
[10] A. Giwa, A. Alabi, A. Yusuf, and T. Olukan, “A comprehensive review on biomass and solar energy for sustainable energy generation in Nigeria,” Renew. Sustain. Energy Rev., vol. 69, pp. 620–641, 2017, doi: 10.1016/j.rser.2016.11.160.
[11] N. Manoj Kumar, K. Sudhakar, and M. Samykano, “Techno-economic analysis of 1 MWp grid connected solar PV plant in Malaysia,” Int. J. Ambient Energy, vol. 40, no. 4, pp. 434–443, 2019, doi: 10.1080/01430750.2017.1410226.
[12] L. Olatomiwa, S. Mekhilef, S. Shamshirband, and D. Petkovic, “Potential of support vector regression for solar radiation prediction in Nigeria,” Nat. Hazards, vol. 77, no. 2, pp. 1055–1068, 2015, doi: 10.1007/s11069-015-1641-x.
[13] M. . Abdulazeez, “Artificial Neural Network Estimation of Global Solar Radiation Using Meteorological Parameters in Gusau , Nigeria,” Appl. Sci. Res., vol. 3, no. 2, pp. 586–595, 2011.
[14] A. A. Adebayo, Mubi region: A geographical synthesis. 2004.
[15] NASA, “Atmospheric Science Data Center,” 2013. https://eosweb.larc.nasa.gov (accessed Dec. 21, 2021).
[16] D. S. Parakh and G. J. Leng, “RETScreen: Clean Energy Management Software,” in Canadian Energy Efficiency Outlook, Government of Canada, 2020, pp. 263–266. doi: 10.1201/9781003151326-22.
[17] F. Cui, C. Park, and M. Kim, “Application of curve-fitting techniques to develop numerical calibration procedures for a river water quality model,” J. Environ. Manage., vol. 249, 2019, doi: 10.1016/j.jenvman.2019.109375.
[18] A. Pacheco-Vega, M. Sen, K. T. Yang, and R. L. McClain, “Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data,” Int. J. Heat Mass Transf., vol. 44, no. 4, pp. 763–770, 2001, doi: 10.1016/S0017-9310(00)00139-3.
[19] S. Shodiya, U. . Mukhtar, and A. . Abdurazaq, “Fuzzy Logic Control of Domestic Airconditioning System for Energy Savings,” Niger. J. Eng. Sci. Technol. Res., vol. 3, no. 2, pp. 57–67, 2017.
[20] Atikpo, “Prediction of vertical distance traveled by Cadmium in Agricultural,” J. Eng. Sci. Appl., pp. 24–31, 2020.
[21] A. K. Azad, M. G. Rasul, and T. Yusaf, “Statistical diagnosis of the best weibull methods for wind power assessment for agricultural applications,” Energies, vol. 7, no. 5, pp. 3056–3085, 2014, doi: 10.3390/en7053056.
[22] K. Mohammadi and A. Mostafaeipour, “Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran,” Energy Convers. Manag., vol. 65, pp. 463–470, 2013, doi: 10.1016/j.enconman.2012.09.004.