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Estimation of the Global Solar Radiation with the Artificial Neural Networks for the City of Sivas

Year 2018, , 46 - 51, 20.06.2018
https://doi.org/10.26701/ems.359681

Abstract

In this study, global solar radiation in the
city of Sivas was estimated by artificial neural networks (ANNs) using
meteorological and geographical data obtained from four different measurement
stations. Mean bias error (MBE), root mean square error (RMSE) and R2
ranged from -1.264 MJ/m2 to 0.938 MJ/m2, 0.710 MJ/m2
to 1.598 MJ/m2 and 0.984 to 0.994, respectively. It is believed that
ANN models could be used to predict global solar radiation for locations where
only the temperature and sunshine duration data are available in the city of
Sivas.

References

  • [1] Hunt L. A., Kuchar L. and Swanton C. J., (1998) “Estimation of solar radiation for use in crop modelling”, Agr. Forest Meteorol., vol.91, pp.293–300
  • [2] Angstrom A., (1924) “Solar and Terrestrial Radiation Report to the International Commission for Solar Research on Actinometric Investigations of Solar and Atmospheric Radiation”, Q. J. R. Meteorol. Soc., vol.50, pp.121–126
  • [3] Rietveld M. R., (1978) “A new Method for Estimating the Regression Coefficients in the Formula Relating Solar Radiation to Sunshine”, Agric. Meteorol., vol.19, pp.243–252
  • [4] Hargreaves G. H. and Samani Z. A., (1982), “Estimating Potential Evapotranspiration”, J. Irrigat Drainage Div. Eng., vol.8, pp.223–230
  • [5] Akinoglu B. G. and Ecevit A., (1990) “Construction of a Quadratic Model Using Modified Angstrom Coefficients to Estimate Global Solar Radiation”, Sol. Energy, vol.45, pp.85–92
  • [6] Alnaser W. E., (1993) “New Model to Estimate the Solar Global Irradiation Using Astronomical and Meteorological Parameters”, Renewable Energy, vol.3, pp.175–177
  • [7] Trabea A. A. and Shaltout M. A. M., (2000) “Correlation of Global Solar-Radiation with Meteorological Parameters over Egypt”, Renewable Energy, vol.21, pp.297–308
  • [8] Almoroks J. and Hontoria C., (2004) “Global Solar Radiation Estimation Using Sunshine Duration in Spain”, Energy Conversion and Management, vol.45, pp.1529–1535
  • [9] Kumar R. and Umanand L., (2005) “Estimation of Global Radiation Using Clearness Index Model for Sizing Photovoltaic System”, Renewable Energy, vol.30, pp.2221–2233
  • [10] Fletcher A. L. and Moot D. J., (2007) Estimating Daily Solar Radiation in New Zealand Using Air Temperatures”, N. Z. J. Crop Hortic. Sci., vol.35, pp.147–157
  • [11] Bakirci K., (2009) “Correlations for Estimation of Daily Global Solar Radiation with Hours of Bright Sunshine in Turkey”, Energy, vol.34, pp.485–501
  • [12] Koussa M., Malek A. and Haddadi M., (2009) “Statistical Comparison of Monthly Mean Hourly and Daily Diffuse and Global Solar Irradiation Models and a Simulink Program Development for Various Algerian Climates”, Energy Convers. Manage., vol.50, pp.1227–1235
  • [13] Omondi A. and Ongoma V., (2015) “Estimation of Mean Monthly Global Solar Radiation Using Sunshine Hours for Nairobi City, Kenya”, J. Renewable and Sustainable Energy, vol.7.
  • [14] Sözen A., Arcaklioğlu E., Özalp M., and Kanit, E. G., (2004) "Use of artificial neural networks for Mapping of Solar Potential in Turkey”. Applied Energy, vol.77, pp.273–86
  • [15] Sözen A., Arcaklioğlu E. and Özalp M., (2004) “Estimation of Solar Potential in Turkey by Artificial Neural Networks Using Meteorological and Geographical Data” Energy Conversion and Management, vol.45, pp.3033–52
  • [16] Koca A., Oztop H. F, Varol Y., and Koca G.O., (2011) “Estimation Of Solar Radiation Using Artificial Neural Networks with Different Input Parameters for Mediterranean Region of Anatoliain Turkey”, Expert Systems with Applications, vol.38, pp.8756–62
  • [17] Şenkal O. and Kuleli T., (2009) “Estimation of Solar Radiation over Turkey Using Artificial Neural Network and Satellite Data”, Applied Energy, vol.86, pp.1222–8
  • [18] Şenkal O., (2010) “Modeling of Solar Radiation Using Remote Sensing and Artificial Neural Network in Turkey”, Energy, vol.35, pp.4795–801
  • [19] Sözen A. and Arcaklioğlu E., (2005) “Solar Potential in Turkey”, Applied Energy, vol.80, pp.35–45
  • [20] Yildiz B. Y , Şahin M., Şenkal O., Pestemalci and Emrahoğlu N. A., (2013) “Comparison of two Solar Radiation Models Using Artificial Neural Networks and Remote Sensing in Turkey” Energy Sources, Part A, vol.35, pp.209–17
  • [21] Şenkal O., Şahin M and Pestemalci V., (2010) “The Estimation of Solar Radiation for Different Time Periods. Energy Sources”, Part A, vol.32, pp.1176–84
  • [22] Şen Z., Öztopal A. and Şahin A. D. (2004) “Solar Irradiation Estimation from Sunshine Duration by Geno-Fuzzy Partial Approach. Energy Sources”, vol.26, pp.377–86
  • [23] Şahin A. D. and Şen Z., (1998) “Statistical Analysis of the Angström Formula Coefficients and Application for Turkey”, Solar Energy, vol.62, pp.29–38
  • [24] Celik A. N. and Muneer T., (2013) “Neural Network Based Method for Conversion of Solar Radiation Data”, Energy Conversion and Management, vol.67, pp.117–24
  • [25] Yadav A. K. and Chandel S.S., (2013) “Solar Radiation Prediction Using Artificial Neural Network Techniques a Review”, Renewable and Sustainable Energy Reviews, vol.33, pp.771-781
  • [26] https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=undefined&m=SIVAS, 2017
  • [27] Lam C. J., Wan K. K. W. and Yang L., (2008) “Solar Radiation Modelling Using Anns for Different Climates in China”, vol.49, pp.1080-1090
  • [28] Görler O. and Akkoyun S., (2017) “Artificial Neural Networks Can be Used as Alternative Method to Estimate Loss Tooth Root Sizes for Prediction of Dental Implants”, C.U. Faculty of Science Science Journal, vol.38, pp.1300-1949
Year 2018, , 46 - 51, 20.06.2018
https://doi.org/10.26701/ems.359681

Abstract

References

  • [1] Hunt L. A., Kuchar L. and Swanton C. J., (1998) “Estimation of solar radiation for use in crop modelling”, Agr. Forest Meteorol., vol.91, pp.293–300
  • [2] Angstrom A., (1924) “Solar and Terrestrial Radiation Report to the International Commission for Solar Research on Actinometric Investigations of Solar and Atmospheric Radiation”, Q. J. R. Meteorol. Soc., vol.50, pp.121–126
  • [3] Rietveld M. R., (1978) “A new Method for Estimating the Regression Coefficients in the Formula Relating Solar Radiation to Sunshine”, Agric. Meteorol., vol.19, pp.243–252
  • [4] Hargreaves G. H. and Samani Z. A., (1982), “Estimating Potential Evapotranspiration”, J. Irrigat Drainage Div. Eng., vol.8, pp.223–230
  • [5] Akinoglu B. G. and Ecevit A., (1990) “Construction of a Quadratic Model Using Modified Angstrom Coefficients to Estimate Global Solar Radiation”, Sol. Energy, vol.45, pp.85–92
  • [6] Alnaser W. E., (1993) “New Model to Estimate the Solar Global Irradiation Using Astronomical and Meteorological Parameters”, Renewable Energy, vol.3, pp.175–177
  • [7] Trabea A. A. and Shaltout M. A. M., (2000) “Correlation of Global Solar-Radiation with Meteorological Parameters over Egypt”, Renewable Energy, vol.21, pp.297–308
  • [8] Almoroks J. and Hontoria C., (2004) “Global Solar Radiation Estimation Using Sunshine Duration in Spain”, Energy Conversion and Management, vol.45, pp.1529–1535
  • [9] Kumar R. and Umanand L., (2005) “Estimation of Global Radiation Using Clearness Index Model for Sizing Photovoltaic System”, Renewable Energy, vol.30, pp.2221–2233
  • [10] Fletcher A. L. and Moot D. J., (2007) Estimating Daily Solar Radiation in New Zealand Using Air Temperatures”, N. Z. J. Crop Hortic. Sci., vol.35, pp.147–157
  • [11] Bakirci K., (2009) “Correlations for Estimation of Daily Global Solar Radiation with Hours of Bright Sunshine in Turkey”, Energy, vol.34, pp.485–501
  • [12] Koussa M., Malek A. and Haddadi M., (2009) “Statistical Comparison of Monthly Mean Hourly and Daily Diffuse and Global Solar Irradiation Models and a Simulink Program Development for Various Algerian Climates”, Energy Convers. Manage., vol.50, pp.1227–1235
  • [13] Omondi A. and Ongoma V., (2015) “Estimation of Mean Monthly Global Solar Radiation Using Sunshine Hours for Nairobi City, Kenya”, J. Renewable and Sustainable Energy, vol.7.
  • [14] Sözen A., Arcaklioğlu E., Özalp M., and Kanit, E. G., (2004) "Use of artificial neural networks for Mapping of Solar Potential in Turkey”. Applied Energy, vol.77, pp.273–86
  • [15] Sözen A., Arcaklioğlu E. and Özalp M., (2004) “Estimation of Solar Potential in Turkey by Artificial Neural Networks Using Meteorological and Geographical Data” Energy Conversion and Management, vol.45, pp.3033–52
  • [16] Koca A., Oztop H. F, Varol Y., and Koca G.O., (2011) “Estimation Of Solar Radiation Using Artificial Neural Networks with Different Input Parameters for Mediterranean Region of Anatoliain Turkey”, Expert Systems with Applications, vol.38, pp.8756–62
  • [17] Şenkal O. and Kuleli T., (2009) “Estimation of Solar Radiation over Turkey Using Artificial Neural Network and Satellite Data”, Applied Energy, vol.86, pp.1222–8
  • [18] Şenkal O., (2010) “Modeling of Solar Radiation Using Remote Sensing and Artificial Neural Network in Turkey”, Energy, vol.35, pp.4795–801
  • [19] Sözen A. and Arcaklioğlu E., (2005) “Solar Potential in Turkey”, Applied Energy, vol.80, pp.35–45
  • [20] Yildiz B. Y , Şahin M., Şenkal O., Pestemalci and Emrahoğlu N. A., (2013) “Comparison of two Solar Radiation Models Using Artificial Neural Networks and Remote Sensing in Turkey” Energy Sources, Part A, vol.35, pp.209–17
  • [21] Şenkal O., Şahin M and Pestemalci V., (2010) “The Estimation of Solar Radiation for Different Time Periods. Energy Sources”, Part A, vol.32, pp.1176–84
  • [22] Şen Z., Öztopal A. and Şahin A. D. (2004) “Solar Irradiation Estimation from Sunshine Duration by Geno-Fuzzy Partial Approach. Energy Sources”, vol.26, pp.377–86
  • [23] Şahin A. D. and Şen Z., (1998) “Statistical Analysis of the Angström Formula Coefficients and Application for Turkey”, Solar Energy, vol.62, pp.29–38
  • [24] Celik A. N. and Muneer T., (2013) “Neural Network Based Method for Conversion of Solar Radiation Data”, Energy Conversion and Management, vol.67, pp.117–24
  • [25] Yadav A. K. and Chandel S.S., (2013) “Solar Radiation Prediction Using Artificial Neural Network Techniques a Review”, Renewable and Sustainable Energy Reviews, vol.33, pp.771-781
  • [26] https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=undefined&m=SIVAS, 2017
  • [27] Lam C. J., Wan K. K. W. and Yang L., (2008) “Solar Radiation Modelling Using Anns for Different Climates in China”, vol.49, pp.1080-1090
  • [28] Görler O. and Akkoyun S., (2017) “Artificial Neural Networks Can be Used as Alternative Method to Estimate Loss Tooth Root Sizes for Prediction of Dental Implants”, C.U. Faculty of Science Science Journal, vol.38, pp.1300-1949
There are 28 citations in total.

Details

Subjects Mechanical Engineering
Journal Section Research Article
Authors

Cahit Gurlek

Mustafa Sahin This is me

Publication Date June 20, 2018
Acceptance Date January 16, 2018
Published in Issue Year 2018

Cite

APA Gurlek, C., & Sahin, M. (2018). Estimation of the Global Solar Radiation with the Artificial Neural Networks for the City of Sivas. European Mechanical Science, 2(2), 46-51. https://doi.org/10.26701/ems.359681

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