Comparative Estimation of Global Solar Radiation over Two Nigerian Cities, Using Artificial Neural Network and Empirical Models
Öz
The estimation of solar radiation intensity has been a focus of many researchers due to the cost of setting up its actual measurements. While many of them employed empirical models, this study utilizes the artificial neural network for the analysis and estimation of global solar radiation over two Nigerian cities. The model developed using sunshine hours, temperatures and relative humidity were compared with the existing empirical models. Model performance indicators comparing the measured data and the computed data for the derived and selected models, using the same number of input meteorological parameters showed that ANN having average values of RMSE, MBE, and MPE of 0.0744 MJm-2day-1, -0.0020 MJm-2day-1, and -0.0043%, respectively, performed slightly better. When different number of input meteorological parameters were used, the ANN gave the following error indicators for RMSE, MBE, MPE of 0.0394MJm-2day-1, -0.0023MJm-2day-1 and -0.0144% respectively. Also, in the result of solar radiation in Abuja, using the same number of meteorological parameters, the model with the best performance in the estimation of solar radiation is the ANN model with average values of RMSE, MBE, MPE of 0.1301MJm-2day-1, 0.0053MJm-2day-1 and 0.0441% respectively. Hence, the models are versatile for predicting global solar radiation in locations in the same climatic zones as locations studied in this study, where direct measurements of solar radiation is scarce and widely separated but there is availability of commonly measured meteorological parameters such as sunshine duration, minimum temperature, maximum temperature and relative humidity.
Anahtar Kelimeler
global solar radiation empirical prediction artificial neural network model
Destekleyen Kurum
The research is self sponsored.
Kaynakça
- [1] Chineke T.C., Nwofor O.K., Okoro U.K., Optimal benefits of utilizing renewable energy technologies in Nigeria and the CIBS Quadrangle-A review, Bayero J. Pure Appl. Sci., ( 2010) 3(1): 142-146.
- [2] Chukwu S.C., Nwachukwu A.N., Analysis of Some Meteorological Parameters Using Artificial Neural Network Method for Makurdi, Nigeria, African Journal of Environmental Science and Technology, (2012). 6(3), 182-188.
- [3] Adesola S.O., Adeniji N.O., Investigation on the Possibility of Using Available Sunshine Duration Data of a Relatively close Region to Estimate Global Solar Radiation for a Different Region, International Journal of Advance scientific Research and Engineering (IJASRE), (2019). ISSN: 2454-8006, Vol. 5, NO/ (2).
- [4] Olatona G.I., Estimating global solar radiation from routine meteorological parameters over a Tropical City (7.23° N; 3.52° E) using quadratic models, Ann. West Univ. Timisoara-Phys., (2018) 60(1): 45-55.
- [5] Osinowo A.A., Okogbue E.C., Ogungbenro S.B., Fashanu.O., Analysis of Global Solar Irradiance over Climatic Zones in Nigeria for Solar Energy Applications, Journal of Solar Energy, (2015). Article, 9 pages.
- [6] Angstrom A., Solar and terrestrial radiation, Quarterly Journal of the Royal Meteorological Society, (1924). 50, 121–126.
- [7] Prescott J.A., Evaporation from a water surface in relation to solar radiation, Transaction of the Royal. Society of South Australia, (1940). 64, 114–125.
- [8] Hargreaves G.H., Samani Z.A., Crop Evapotranspiration from Temperature, Applied Engineering in Agriculture, (1985), 1, 96-99.
- [9] Mubiru J., Banda E. J. “Prediction of monthly average daily global solar irradiation using artificial neural networks,” Solar Energy, (2008) 82 (2), 181–187.
- [10] Tymvios F.S., Jacovides C.P., Michaelides S.C., Scouteli C.,. Comparative study of Angstrom’s and artificial neural networks methodologies in estimating global solar radiation, Solar Energy, (2005) 78, 752–762.
- [11] Olatona G.I., Adeleke E. A., Estimation of solar radiation over Ibadan from routine meteorological parameters, Journal of Engineering and Sciences, 4(3) 44-51.
- [12] Tikyaaet E.V., Akinbolati, A., Shehu M., Assessment of empirical models for estimating mean monthly global solar radiation in katsina, FUDMA Journal of Sciences (FJS), (2019) 3(1) 333 – 344.
- [13] Okogbue, E.C., Adedokun, J.A., Holmgren, B., Hourly and daily clearness index and diffuse fraction at a tropical station, Ile-Ife, Nigeria, Int. J. Climatol, (2009) 29, 1035–1047.
- [14] Falaiye, O.A., Babatunde, E.B., Willoughby, A.A., Atmospheric aerosol loading at Ilorin, a tropical station, Afr. Rev. Phys., (2014). 9 (0065), 527–535
Öz
Kaynakça
- [1] Chineke T.C., Nwofor O.K., Okoro U.K., Optimal benefits of utilizing renewable energy technologies in Nigeria and the CIBS Quadrangle-A review, Bayero J. Pure Appl. Sci., ( 2010) 3(1): 142-146.
- [2] Chukwu S.C., Nwachukwu A.N., Analysis of Some Meteorological Parameters Using Artificial Neural Network Method for Makurdi, Nigeria, African Journal of Environmental Science and Technology, (2012). 6(3), 182-188.
- [3] Adesola S.O., Adeniji N.O., Investigation on the Possibility of Using Available Sunshine Duration Data of a Relatively close Region to Estimate Global Solar Radiation for a Different Region, International Journal of Advance scientific Research and Engineering (IJASRE), (2019). ISSN: 2454-8006, Vol. 5, NO/ (2).
- [4] Olatona G.I., Estimating global solar radiation from routine meteorological parameters over a Tropical City (7.23° N; 3.52° E) using quadratic models, Ann. West Univ. Timisoara-Phys., (2018) 60(1): 45-55.
- [5] Osinowo A.A., Okogbue E.C., Ogungbenro S.B., Fashanu.O., Analysis of Global Solar Irradiance over Climatic Zones in Nigeria for Solar Energy Applications, Journal of Solar Energy, (2015). Article, 9 pages.
- [6] Angstrom A., Solar and terrestrial radiation, Quarterly Journal of the Royal Meteorological Society, (1924). 50, 121–126.
- [7] Prescott J.A., Evaporation from a water surface in relation to solar radiation, Transaction of the Royal. Society of South Australia, (1940). 64, 114–125.
- [8] Hargreaves G.H., Samani Z.A., Crop Evapotranspiration from Temperature, Applied Engineering in Agriculture, (1985), 1, 96-99.
- [9] Mubiru J., Banda E. J. “Prediction of monthly average daily global solar irradiation using artificial neural networks,” Solar Energy, (2008) 82 (2), 181–187.
- [10] Tymvios F.S., Jacovides C.P., Michaelides S.C., Scouteli C.,. Comparative study of Angstrom’s and artificial neural networks methodologies in estimating global solar radiation, Solar Energy, (2005) 78, 752–762.
- [11] Olatona G.I., Adeleke E. A., Estimation of solar radiation over Ibadan from routine meteorological parameters, Journal of Engineering and Sciences, 4(3) 44-51.
- [12] Tikyaaet E.V., Akinbolati, A., Shehu M., Assessment of empirical models for estimating mean monthly global solar radiation in katsina, FUDMA Journal of Sciences (FJS), (2019) 3(1) 333 – 344.
- [13] Okogbue, E.C., Adedokun, J.A., Holmgren, B., Hourly and daily clearness index and diffuse fraction at a tropical station, Ile-Ife, Nigeria, Int. J. Climatol, (2009) 29, 1035–1047.
- [14] Falaiye, O.A., Babatunde, E.B., Willoughby, A.A., Atmospheric aerosol loading at Ilorin, a tropical station, Afr. Rev. Phys., (2014). 9 (0065), 527–535
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Çevre Bilimleri |
| Bölüm | Natural Sciences |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Haziran 2023 |
| Gönderilme Tarihi | 4 Kasım 2022 |
| Kabul Tarihi | 16 Haziran 2023 |
| Yayımlandığı Sayı | Yıl 2023Cilt: 44 Sayı: 2 |