Estimation of Measured Global Solar Radiation by Artificial Neural Networks for Mersin / Turkey and Comparison with Common Solar Radiation Models

Year 2019, Volume: 7 Issue: 1, 80 - 96, 24.03.2019

Gökhan Arslan , Burhan Bayhan , Kaan Yaman

https://doi.org/10.29109/gujsc.419473

Cited By: 8

Abstract

In
this study, the daily total global solar radiation values measured for Mersin
between April 2017 and March 2018 were modeled using artificial neural networks
and the performance of estimating daily total global solar radiation values of
the common models in the literature was investigated. Daily average air
temperature, relative humidity, wind speed, sunshine duration, and cloud cover
data are obtained from the Turkish State Meteorological Service and solar
radiation values are measured with a pyranometer. As a result, Model 37 (Yaman
ve Arslan) showed the best prediction performance among the models examined,
with the coefficient of determination (R²) being 0.83. the
coefficient of determination (R²) was obtained 0.75 for artificial
neural network that use dry bulb temperature, relative humidity and wind speed.

Keywords

global solar radiation, artificial neural network, Mersin

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

Abstract

Bu çalışmada, Nisan
2017 – Mart 2018 tarihleri aralığında Mersin için ölçülen günlük toplam global
güneş ışınım değerlerinin yapay sinir ağı kullanılarak modellenmesi yapılmıştır
ve literatürde bulunan yaygın modellerin günlük toplam global güneş ışınım değerlerini
tahmin etme performansları incelenmiştir. Günlük ortalama hava sıcaklığı, bağıl
nem, rüzgar hızı, güneşlenme süresi ve bulut kapalılığı verileri, Devlet
Meteoroloji İşleri Genel Müdürlüğü’nden temin edilmiş olup güneş ışnım
değerleri ise piranometre ile ölçülmüştür. Sonuç olarak, incelenen modeller
içerisinde en iyi tahmin performansını belirlilik katsayısı (R²)
0,83 olan Model 37 (Yaman ve Arslan) göstermiştir. Kuru termometre sıcaklığı,
bağıl nem ve rüzgar hızına bağlı güneş ışınımını tahmin eden yapa sinir ağının
belirlilik katsayısı (R²) 0,75 olmuştur.

Keywords

Global güneş ışınımı, yapay sinir ağı, Mersin

References

• [1] B. Bayhan, G. Arslan, Applicability of solar and wind energy technologies for a non-residential building. Turkish Journal of Engineering, 2: 1 (2018) 27-34.
• [2] G. Arslan, B. Bayhan, Solar energy potential in Mersin and a simple model to predict daily solar radiation. Mugla Journal of Science and Technology, Special Issue (2016) 1-4.
• [3] M. Şahan, Y. Okur, Akdeniz bölgesine ait meteorolojik veriler kullanılarak yapay sinir ağları yardımıyla güneş enerjisinin tahmini, SDÜ Fen Edebiyat Fakültesi Fen Dergisi, 11:1 (2016) 61-71.
• [4] M. A. Alsaad, Characteristic distribution of global radiation for Amman, Jordan, Solar Wind Technol., 7 (1990) 261–266.
• [5] S. Jain, P. C. Jain, A comparison of the Angstrom-type correlations and the estimation of monthly average daily global irradiation, Solar Energy, 40 (1988) 93–98.
• [6] J. E. Hay, Calculation of monthly mean solar radiation for horizontal and inclined surfaces, Solar Energy 23 (1979) 301-307.
• [7], P. V. C. Luhanga, J. Andringa, Characteristic of solar radiation at Sebele, Gaborone, Botswana, Solar Energy, 44 (1990) 71–81.
• [8] J. Almorox, C. Hontoria, Global solar radiation estimation using sunshine duration in Spain, Energy Convers.Manage., 45 (2004) 1529–1535.
• [9] M. Ozturk, An Evaluation of Global Solar Radiation Empirical Formulations in Isparta, Turkey, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 37 (2015) 2474–2486.
• [10] V. Bahel, H. Bakhsh, R. Srinivasan, A correlation for estimation of global solar radiation, Energy, 12 (1987) 131–135.
• [11] A. Louche, G. Notton, P. Poggi, G. Simonnot, Correlations for direct normal and global horizontal irradiation on a French Mediterranean site, Solar Energy, 46 (1991) 261–266.
• [12] B. G. Akinoğlu, A. Ecevit, A further comparison and discussion of sunshine based models to estimate global solar radiation, Solar Energy, 15 (1990) 865–872.
• [13] H. Ogelman, A. Ecevit, E. Tasdemiroglu, A new method for estimating solar radiation from bright sunshine data, Solar Energy, 33 (1984) 619–625.
• [14] E. Tasdemiroglu, R. Sever, An improved correlation for estimating solar radiation from bright sunshine data for Turkey, Energy Convers. Manage., 31 (1991b) 599–600.
• [15] B. Aksoy, Estimated monthly average global radiation for Turkey and its comparison with observations, Renewable Energy, 10 (1997) 625–633.
• [16] R. Said, M. Mansor, T. Abuain, Estimation of global and diffuse radiation at Tripoli, Renewable Energy, 14 (1998) 221–227.
• [17] I. T. Togrul, H.Togrul, Global solar radiation over Turkey: Comparison of predicted and measured data, Renewable Energy, 25 (2002) 55–67.
• [18] S. Tahran, A. Sarı, Model selection for global and diffuse radiation over the Central Black Sea (CBS) region of Turkey, Energy Convers. Manage., 46 (2005) 605–613.
• [19] Z. Jin, W. Yezheng, Y. Gang, General formula for estimation of monthly average daily global solar radiation in China, Energy Convers. Manage., 46 (2005) 257–268.
• [20] H. Aras, O. Balli, A. Hepbasli, Global solar radiation Potential. Part 1. Model development, Energy Sources Part B, 1 (2006) 303–315.
• [21] V. Bahel, R. Srinivasan, H. Bakhsh, Solar radiation for Dhahran, Saudi Arabia, Energy ,11 (1986) 985–989.
• [22] T. D. M. A. Samuel, Estimation of global radiation for Sri Lanka, Solar Energy, 47 (1991) 333–337.
• [23] G. Lewis, An empirical relation for estimating global irradiation for Tennessee, USA, Energy Convers. Manage., 33 (1992) 1097–1099.
• [24] K. Ulgen, A. Hepbasli, Solar radiation models. Part 2: Comparison and developing new models, Energy Sources, 26 (2004) 521–530.
• [25] A. Kılıç, A. Öztürk, Güneş enerjisi. Kipaş Dağıtımcılık, (1983).
• [26] H. Bulut, O. Büyükalaca, Simple Model For The Generation of Daily Global Solar-Radiation Data in Turkey, Applied Energy, 84 (2007) 477-491.
• [27] G. H. Hargreaves, Z. A. Samani, J. Irrig. and Drain. Engr., 108 (1982) 223.
• [28] K. L. Bristow, G. S. Campbell, Agric. For. Meteorol., 31 (1984) 159.
• [29] M. Donatelli, G. S. Campbell, A simple model to estimate global solar radiation. Proceedings of the 5th European Society of Agronomy Congress, Nitra, (Slovak Republic 1998), p.133.
• [30] D. G. Goodin, J. M. S. Hutchinson, R. L. Vanderlip, M. C. Knapp, Agron. J., 91 (1999) 845.
• [31] J. G. Annandale, N. Z. Jovanic, N. Benade, R. G. Allen, Irrig. Sci., 21 (2002) 57.
• [32] M. Bou-Rabee, et al., Using artificial neural networks to estimate solar radiation in Kuwait. Renewable and Sustainable Energy Reviews, 72 (2017) 434-438.
• [33] M. Vakili, et al., Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data. Journal of cleaner production, 141 (2017) 1275-1285.
• [34] X. Xue, Prediction of daily diffuse solar radiation using artificial neural networks. International Journal of Hydrogen Energy, 42:47 (2017) 28214-28221.
• [35] C. Renno, F. Petito, A. Gatto, ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building. Journal of Cleaner Production 135 (2016) 1298-1316.
• [36] L. Zou, et al., Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China. Journal of Atmospheric and Solar-Terrestrial Physics, 146 (2016) 110-122.
• [37] M. Sinecen, B. Kaya, Ö. Yıldız, Aydın İlinde İnsan Sağlığını Birincil Dereceden Etkileyen Hava Değişkenlerine Yönelik Yapay Sinir Ağı Tabanlı Erken Uyarı Modeli. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5:4 (2017) 121-131.
• [38] F. Yıldız, İ. Gurer, Sultansazlığı sulak alanı için buharlaşma yöntemlerinin karşılaştırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 2:3 (2014) 247-254.
• [39] http://www.enerjiatlasi.com Erişim tarihi: 23 Nisan 2018.
• [40] M. Tırıs, C. Tırıs, Y. Erdalli, Water heating systems by solar energy. Marmara Research Centre, Institute of Energy Systems and Environmental Research, Nato Tucoating, Gebze, Kocaeli, Turkey (1997).
• [41] A. Angstrom, Solar and terrestrial radiation, Q. J. R. Meteorolog. Soc., 50 (1924) 121–125.
• [42] E. Deniz, K. Atik, Güneş ışınım şiddeti tahminlerinde yapay sinir ağları ve regresyon analiz yöntemleri kullanımının incelenmesi. Isı bilimi ve tekniği dergisi, 27: 2 (2007) 15-20.
• [43] R. Trippi, E. Turban, Neural Networks in Finance and Investing. Chicago:Irwin Publishing, 1996.
• [44] L. H. Tsaukalas, R. E. Uhrig, Fuzzy and Neural Approaches in Engineering. New York: Wiley, 1997.
• [45] A. Zafari, M. H. Kianmehr, R. Abdolahzadeh, Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network, International Journal Of Recycling of Organic Waste in Agriculture 2.1 (2013) 9.
• [46] Ö. Çelik, A. Teke, H. B. Yıldırım, The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey. Journal of cleaner production, 116 (2016) 1-12.
• [47] A. Rahimikhoob, Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew. energy, 35 (2010) 2131-2135.
• [48] A. Sozen, E. Arcaklioglu, M. Ozalp, Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data. Energy Convers. Manag., 45 (2004) 3033-3052.
• [49] D. A. Fadare, Modelling of solar energy potential in Nigeria using an artificial neural network model. Appl. energy, 86 (2009) 1410-1422.
• [50] M. Benghanem, A. Mellit, S. N. Alamri, ANN-based modelling and estimation of daily global solar radiation data: a case study. Energy Convers. Manag., 50 (2009) 1644-1655.
• [51] H. K. Elminir, Y.A. Azzam, F. I. Younes, Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models. Energy, 32 (2007) 1513-1523.
• [52] A. Koca, H. F. Oztop, Y. Varol, G. O. Koca, Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey. Expert Syst. Appl., 38 (2011) 8756-8762.
• [53] M. A. Behrang, E. Assareh, A. Ghanbarzadeh, A. R. Noghrehabadi, The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Sol. energy, 84 (2010) 1468-1480.
• [54] G. A. Agbo, G. F. Ibeh, J. E. Ekpe, Estimation of global solar radiation at Onitsha with regression analysis and artificial neural network models. Res. J. Recent Sci., 1 (2012) 27-31.
• [55] M. Ozgoren, M. Bilgili, B. Sahin, Estimation of global solar radiation using ANN over Turkey. Expert Syst. Appl., 39 (2012) 5043-5051.
• [56] S. Rehman, M. Mohandes, Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy policy, 36 (2008) 571-576.
• [57] A. Sfetsos, A. H. Coonick, Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Sol. energy, 68 (2000) 169-178.
• [58] J. Mubiru E. J. K. B. Banda, Estimation of monthly average daily global solar irradiation using artificial neural networks. Sol. energy, 82 (2008) 181-187.
• [59] J. Qin, Z. Chen, K. Yang, S. Liang, W. Tang, Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products. Appl. energy ,88 (2011) 2480-2489.
• [60] M. Mohandes, S. Rehman, T. O. Halawani, Estimation of global solar radiation using artificial neural networks. Renew. energy, 14 (1998) 179-184.
• [61] B. Amrouche, L.X. Pivert, Artificial neural network based daily local forecasting for global solar radiation. Appl. energy, 130 (2014) 333-341.
• [62] A.S.S. Dorvlo, J.A. Jervase, A. Al-Lawati, Solar radiation estimation using artificial neural networks. Appl. energy, 71 (2002) 307-319.
• [63] Q. Y. Zhang, Y. J. Huang, Development of Typical Year Weather Files for Chinese Locations, ASHRAE Transactions, 108 (2002) 1063-1075.
• [64] K. Yaman, G. Arslan, The impact of hourly solar radiation model on building energy analysis in different climatic regions of Turkey, Building Simulation, 11:3 (2018) 483-495.