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Year 2021, Volume: 9 Issue: 3, 427 - 432, 30.09.2021
https://doi.org/10.21541/apjes.894390

Abstract

References

  • [1] IEA, Countries: Turkey, URL: http://iea.org (Reached; January, 1, 2021)
  • [2] The Ministry of Energy and Natural Resources, Information Center, URL: http://energy.gov.tr (Reached; August, 14, 2020)
  • [3] BP Statistical Review of World Energy (2017), URL: http://bp.com (Reached; May, 1, 2020)
  • [4] D.S.H. Chan, J.C.H. Phang, “Analytical methods for the extraction of solar cell single-double model parameters from I-V characteristics”, IEEE Transactions on Electron Devices, 34(2), pp.286-293, 1987.
  • [5] V.L. Brano, A. Orioli, G. Ciulla, A.D: Gangi, “An improved five-parameter model for photovoltaic modules”, Solar Energy Materials and Solar Cells, 94, pp. 1358-1370, 2010.
  • [6] F. Ghani, G. Rosengarten, M. Duke, J.K. Carson, “The numerical calculation of single-diode solar-cell modelling parameters”, Renewable Energy, 72, pp.105-112, 2014.
  • [7] Photovoltaic System Performance Monitoring-Guideliness for Measurement, Data Exchange and Analysis, IEC Standard 61724, 1998.
  • [8] The Ministry of Energy and Natural Resources, Solar Energy Potential Atlas (2019), URL: http://yegm.gov.tr (Reached; February, 2, 2020)
  • [9] J.K. Page, “The estimation of monthly mean values of daily total short wave radiation on vertical and inclined surface from sunshine records for latitudes 40N-40S”, UN Conference on New Sources of Energy, Rome, 1961.
  • [10] H. Aras, O. Ballı, A. Hepbaşlı, “Estimating the horizontal diffuse solar radiation over the Central Anatolia Region of Turkey”, Energy Convers Manage, 47, pp.2240-2249, 2006.
  • [11] S. Tarhan, A. Sari, “Model selection for global and diffuse radiation over the Central Black Sea (CBS) region of Turkey”, Energy Convers Manage, 46(4), pp.605–13, 2005.
  • [12] Y. Jiang, “Estimation of monthly mean daily diffuse radiation in China”, Applied Energy, 86:1458, 2009.
  • [13] H. Khorasanizadeh, K. Mohammadi, A. Mostafaeipour, “Establishing a diffuse solar radiation model for determining the optimum tilt angle of solar surfaces in Tabass, Iran”, Energy Convers Manage, 78, pp.805–814, 2014.
  • [14] S. Barbaro, G. Cannata, S. Coppolino, C. Leone, E. Sinagra, “Diffuse solar radiation statistics for Italy”, Solar Energy, 26, pp. 429-435, 1981.
  • [15] M. Iqbal, “Correlation of average diffuse and beam R-radiation with hours of bright sunshine”, Solar Energy, 23(2), pp.169-173, 1979.
  • [16] G. Lewis, “Diffuse Irradiation over Zimbabwe”, Sol Energy, 31(1), pp.125–128, 1983.
  • [17] K.K. Gopinathan, “Computing the monthly mean daily diffuse radiation from clearness index and percent possible sunshine”, Sol Energy, 41(4), pp.379–385, 1988.
  • [18] D.G. Erbs, S.A. Klein, J.A. Duffie, “Estimation of the Diffuse Radiation Fraction for Hourly, Daily and Monthly Average Global Radiation”, Solar Energy, 28, pp. 293-302, 1982.
  • [19] C.A. Tirmikci, C. Yavuz, “Establishing new regression equations for obtaining the diffuse solar radiation in Sakarya (Turkey)”, Tehnicki Vjesnik-Technical Gazette, Vol.25, pp.503, 2018.
  • [20] J. Kou et al., “Photovoltaic power forecasting based on artificial neural network and meteorological data”, TENCON 2013-2013 IEEE Region 10 Conference, Xian, China.
  • [21] X. Qing, Y. Niu, “Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM”, Energy, 148, pp.462-468, 2018.
  • [22] Maitanova M, Telle JS, Hanke B, Grottke M, Schmidt T, von Maydell K, Agert C, “A machine learning approach to low-cost photovoltaic power prediction based on publicly available weather reports”, Energies, 13, vol. 735-757, 2020.
  • [23] Weather Kocaeli, URL: https://www.meteoblue.com/ (Reached; April, 5, 2020)

Investigating the Effects of Temperature and Relative Humidity on Performance Ratio of a Grid Connected Photovoltaic System

Year 2021, Volume: 9 Issue: 3, 427 - 432, 30.09.2021
https://doi.org/10.21541/apjes.894390

Abstract

The size and the cost of photovoltaic (PV) systems are dependent on the performance ratio of solar cells. Current studies in literature usually determines the size according to the total solar radiation received on the surface of solar modules. However, it is a fact that the energy output of a solar module is also effected by weather conditions of the location where the system is mounted. Thus for an efficient design, weather conditions must be taken into consideration to determine the size. In this paper the performance ratio of an existing photovoltaic system was established and the effect of weather conditions on the energy conversion was analyzed. For this purpose, the reference yield of the system was estimated in terms of solar radiation components received on the surface of solar modules for a specific period. Then the performance ratio was calculated by dividing the measured final yield to the estimated reference yield. In conclusion the change in performance ratio was discussed for different temperature and relative humidity values. Finally, the effect of meteorological inputs on PV system performance is investigated based on a back propagation artificial neural network approach. In conclusion, theoretical and computational results are evaluated.

References

  • [1] IEA, Countries: Turkey, URL: http://iea.org (Reached; January, 1, 2021)
  • [2] The Ministry of Energy and Natural Resources, Information Center, URL: http://energy.gov.tr (Reached; August, 14, 2020)
  • [3] BP Statistical Review of World Energy (2017), URL: http://bp.com (Reached; May, 1, 2020)
  • [4] D.S.H. Chan, J.C.H. Phang, “Analytical methods for the extraction of solar cell single-double model parameters from I-V characteristics”, IEEE Transactions on Electron Devices, 34(2), pp.286-293, 1987.
  • [5] V.L. Brano, A. Orioli, G. Ciulla, A.D: Gangi, “An improved five-parameter model for photovoltaic modules”, Solar Energy Materials and Solar Cells, 94, pp. 1358-1370, 2010.
  • [6] F. Ghani, G. Rosengarten, M. Duke, J.K. Carson, “The numerical calculation of single-diode solar-cell modelling parameters”, Renewable Energy, 72, pp.105-112, 2014.
  • [7] Photovoltaic System Performance Monitoring-Guideliness for Measurement, Data Exchange and Analysis, IEC Standard 61724, 1998.
  • [8] The Ministry of Energy and Natural Resources, Solar Energy Potential Atlas (2019), URL: http://yegm.gov.tr (Reached; February, 2, 2020)
  • [9] J.K. Page, “The estimation of monthly mean values of daily total short wave radiation on vertical and inclined surface from sunshine records for latitudes 40N-40S”, UN Conference on New Sources of Energy, Rome, 1961.
  • [10] H. Aras, O. Ballı, A. Hepbaşlı, “Estimating the horizontal diffuse solar radiation over the Central Anatolia Region of Turkey”, Energy Convers Manage, 47, pp.2240-2249, 2006.
  • [11] S. Tarhan, A. Sari, “Model selection for global and diffuse radiation over the Central Black Sea (CBS) region of Turkey”, Energy Convers Manage, 46(4), pp.605–13, 2005.
  • [12] Y. Jiang, “Estimation of monthly mean daily diffuse radiation in China”, Applied Energy, 86:1458, 2009.
  • [13] H. Khorasanizadeh, K. Mohammadi, A. Mostafaeipour, “Establishing a diffuse solar radiation model for determining the optimum tilt angle of solar surfaces in Tabass, Iran”, Energy Convers Manage, 78, pp.805–814, 2014.
  • [14] S. Barbaro, G. Cannata, S. Coppolino, C. Leone, E. Sinagra, “Diffuse solar radiation statistics for Italy”, Solar Energy, 26, pp. 429-435, 1981.
  • [15] M. Iqbal, “Correlation of average diffuse and beam R-radiation with hours of bright sunshine”, Solar Energy, 23(2), pp.169-173, 1979.
  • [16] G. Lewis, “Diffuse Irradiation over Zimbabwe”, Sol Energy, 31(1), pp.125–128, 1983.
  • [17] K.K. Gopinathan, “Computing the monthly mean daily diffuse radiation from clearness index and percent possible sunshine”, Sol Energy, 41(4), pp.379–385, 1988.
  • [18] D.G. Erbs, S.A. Klein, J.A. Duffie, “Estimation of the Diffuse Radiation Fraction for Hourly, Daily and Monthly Average Global Radiation”, Solar Energy, 28, pp. 293-302, 1982.
  • [19] C.A. Tirmikci, C. Yavuz, “Establishing new regression equations for obtaining the diffuse solar radiation in Sakarya (Turkey)”, Tehnicki Vjesnik-Technical Gazette, Vol.25, pp.503, 2018.
  • [20] J. Kou et al., “Photovoltaic power forecasting based on artificial neural network and meteorological data”, TENCON 2013-2013 IEEE Region 10 Conference, Xian, China.
  • [21] X. Qing, Y. Niu, “Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM”, Energy, 148, pp.462-468, 2018.
  • [22] Maitanova M, Telle JS, Hanke B, Grottke M, Schmidt T, von Maydell K, Agert C, “A machine learning approach to low-cost photovoltaic power prediction based on publicly available weather reports”, Energies, 13, vol. 735-757, 2020.
  • [23] Weather Kocaeli, URL: https://www.meteoblue.com/ (Reached; April, 5, 2020)
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ceyda Aksoy Tırmıkçı 0000-0003-0354-4022

Cenk Yavuz 0000-0002-4325-2852

Talha Enes Gümüş 0000-0002-6716-6414

Publication Date September 30, 2021
Submission Date March 10, 2021
Published in Issue Year 2021 Volume: 9 Issue: 3

Cite

IEEE C. Aksoy Tırmıkçı, C. Yavuz, and T. E. Gümüş, “Investigating the Effects of Temperature and Relative Humidity on Performance Ratio of a Grid Connected Photovoltaic System”, APJES, vol. 9, no. 3, pp. 427–432, 2021, doi: 10.21541/apjes.894390.