A single hybrid parameter-based model for calibrating hargreaves-samani coefficient in Nigeria

  • Authors

    • Samuel Nwokolo University of Calabar
    • Julie Ogbulezie University of Calabar
    2017-08-20
    https://doi.org/10.14419/ijpr.v5i2.8042
  • , Hargreaves-Samani Model, Calibration, Hybrid Parameter-Based Model, Global Solar Radiation, Coastal Region, Interior Region.

  • Abstract

    The present research was designed to locally calibrate Hargreaves-Samani computing model (HS) in twenty-one (21) locations with their corresponding coastal and interior regions in Nigeria employing a single hybrid parameter-based model to obtain the adjusted Hargreaves-Samani coefficient (AHC) for Nigerian environment. To achieve this purpose, meteorological parameters such as extraterrestrial solar radiation, maximum sunshine duration, minimum and maximum temperatures were employed as input parameters to compute the original HS model and equally calibrate the original HS model. The monthly AHCs were obtained by multiplying the 0.17 by the monthly ratio of the observed global solar radiation (H) to H calculated from original HS model. The average value was obtained per station. These observed AHC values were considered as the target values for the development of hybrid parameter-based models (HP) for every station used for calibrating original HS model. On the whole, the result from the statistical indicators confirmed that the locally calibrated HS model performed better than the original HS model in all stations (including coastal and interior regions) investigated. Both the original and calibrated HS models underestimated H at annual timescale, but the calibrated HS model provided closer average values with H, which could confirm the good performances of the calibrated HS model. Therefore, the calibrated HS model obtained in this research could be highly recommended for estimating H in Nigeria when only temperature data are available.

  • References

    1. [1] Manzano, A, Martin, M.L., Valero, F., & Armenta, C. (2015). A single method to estimate daily global solar radiation from monthly data, 166, 70-82

      [2] Boukelia,T.E., Mecibah, M.S., & Meriche, I.E. (2014). General models for estimation of the monthly mean daily diffuse solar radiation (Case study:Algeria). Energy Conversion and Management, 31, 211-219 https://doi.org/10.1016/j.enconman.2014.02.035.

      [3] Nwokolo, S.C., & Ogbulezie, J.C., (2017).A Quantitative Review and Classification of Empirical Models for Predicting Global Solar Radiation in West Africa. Beni-Suef University Journal of Basic and Applied Sciences, (In Press). https://doi.org/10.1016/j.bjbas.2017.05.001.

      [4] Souza, J.L., Lyra, G.B., Sanobs, C.M.D., Feneiru-Junior, R.A., Tibu, C., Lyra, G.B., & Lemes, M.A.M. (2016). Empirical models of daily and monthly global solar irradiation using sunshine duration for Alagoas State, Northeastern Brazil. Sustainable Energy Technologies and Assessments, 14, 35-45. https://doi.org/10.1016/j.seta.2016.01.002.

      [5] Li, H., Bu, X., Long, Z., Zhao, L., & Ma, W., (2012). Calculating the diffuse solar radiation in regions without solar radiation measurements. Energy, 44, 611-615. https://doi.org/10.1016/j.energy.2012.05.033.

      [6] Nwokolo, S.C. (2017). A comprehensive review of empirical models for estimating global solar radiation in Africa. Renewable and Sustainable Energy Reviews, 78: 955–995 https://doi.org/10.1016/j.rser.2017.04.101.

      [7] Khorasanizadeh, H., & Mohammadi, G. (2016). Diffuse solar radiation on a horizontal surface: Reviewing and categorizing the empirical models. Renewable and Sustaining Energy Reviews, 53, 338-362. https://doi.org/10.1016/j.rser.2015.08.037.

      [8] Thirugnanasasambandam, M., Iniyan, S., & Goic, A. (2010). A review of solar thermal technologies. Renewable and Sustainable Energy Reviews, 14, 312-322. https://doi.org/10.1016/j.rser.2009.07.014.

      [9] Ullah, K.R., Saidur, R., Ping, H.W., Akikur, R.K., & Shuvo, N.H. (2013) A review of solar thermal refrigeration and cooling methods. Renewable and Sustainable Energy Review, 24, 499-513. https://doi.org/10.1016/j.rser.2013.03.024.

      [10] Angstrom, A. (1924). Solar and terrestrial radiation. Quarterly Journal of Royal Meteorological Society, 50, 121-5. https://doi.org/10.1002/qj.49705021008.

      [11] Prescott, J.A. (1940). Evaporation from water surface in relation to solar radiation. Transactions of the Royal Society of Australia, 48, 114-118.

      [12] Page, I.K. (1960). The estimation of monthly mean values of daily total shortwave radiation in vertical and inclined surfaces from sunshine records for latitude 400N – 400S. In Proceedings of UN Conference on new Source of Energy, 378 – 390

      [13] Hargreaves, G.H., & Samani, Z.A. (1982). Estimating potential evaporation. Journal of Irrigation and Drainage Engineering, 108, 223-230.

      [14] Bristow, K.L., & Campbell, G.S. (1984). On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agriculture and Forest Meteorology, 31, 159-166 https://doi.org/10.1016/0168-1923(84)90017-0.

      [15] Thornton, P.E., & Running, S.W. (1999). An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity and precipitation. Agriculture and Forest Meteorology, 93, 211-228 https://doi.org/10.1016/S0168-1923(98)00126-9.

      [16] Kimball, H.H. (1919). Variations in the total and luminous solar radiation with geographical position in the United State. Monthly Weather Research, 47, 769-793 https://doi.org/10.1175/1520-0493(1919)47<769:VITTAL>2.0.CO;2.

      [17] Rietveld, M.R. (1978). A new method for estimating the regression coefficients in the formulae relating solar radiation to sunshine. Agriculture and Meteorology, 19, 243-252 https://doi.org/10.1016/0002-1571(78)90014-6.

      [18] Akpabio, L.E., Udo, S.O., & Etuk, S.E. (2005). Modeling global solar radiation for a tropical location: Onne, Nigeria. Turkish Journal of Physics, 29, 63-68.

      [19] Falayi, E.O., & Rabiu, A.B. (2005). Modeling global solar radiation using sunshine duration data. Nigeria Journal of Physics, 17, 181-186

      [20] Bayat, K., & Mirlatifi, S.M. (2009). Estimation of daily global solar radiation using regression models and artificial neural network. Agriculture’s Science and Natural Resources Magazine, 16:3[in Persian]

      [21] Ohunakin, O.S., Adaramola, M.S., Oyewolu, O.M., & Fagbenle, R.O. (2013). Correlations for estimating solar radiation using sunshine hours and temperature measurement in OSogbo, Osun State, Nigeria. Front Energy, 13, 1-9 https://doi.org/10.1007/s11708-013-0241-2.

      [22] Adaramola, M.S. (2012). Estimating global solar radiation using common meteorological data in Akura, Nigeria. Renewable Energy, 47, 38-44. https://doi.org/10.1016/j.renene.2012.04.005.

      [23] Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop evapotranspiration guideline for computing crop water requirement. FAO Irrigation and Drainage Paper, Rome, Italy. 56, P. 290.

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  • How to Cite

    Nwokolo, S., & Ogbulezie, J. (2017). A single hybrid parameter-based model for calibrating hargreaves-samani coefficient in Nigeria. International Journal of Physical Research, 5(2), 49-59. https://doi.org/10.14419/ijpr.v5i2.8042

    Received date: 2017-06-26

    Accepted date: 2017-08-07

    Published date: 2017-08-20