Abstract.
Accurate estimation of solar radiation is the major concern in renewable energy applications. Over the past few years, a lot of machine learning paradigms have been proposed in order to improve the estimation performances, mostly based on artificial neural networks, fuzzy logic, support vector machine and adaptive neuro-fuzzy inference system. The aim of this work is the prediction of the daily global solar radiation, received on a horizontal surface through the Gaussian process regression (GPR) methodology. A case study of Ghardaïa region (Algeria) has been used in order to validate the above methodology. In fact, several combinations have been tested; it was found that, GPR-model based on sunshine duration, minimum air temperature and relative humidity gives the best results in term of mean absolute bias error (MBE), root mean square error (RMSE), relative mean square error (rRMSE), and correlation coefficient (r) . The obtained values of these indicators are 0.67 MJ/m2, 1.15 MJ/m2, 5.2%, and 98.42%, respectively.
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References
H. Othenio, J. Awange, Energy Resources in Africa (Springer, 2016)
A.S.S. Dorolvo, D.B. Ampratwum, Renew. Energy 17, 421 (1999)
J.Y. Almorox, C. Hontoria, Energy Convers. Manag. 45, 1529 (2004)
M. Benghanem, A.A. Joraid, Saudi Arabia. Renew. Energy 32, 2424 (2007)
D.B. Ampratwum, A.S.S. Dorolvo, Appl. Energy 63, 161 (1999)
Y.A.G. Abdallah, Int. J. Sol. Energy 16, 111 (1994)
H. Duzen, H. Aydin, Energy Convers. Manag. 58, 35 (2012)
S. Benkaciali et al., Rev. Energies Renouv. 19, 617 (2016)
Mawloud Guermoui et al., Leonardo Electron. J. Pract. Technol. 15, 35 (2016)
J. Almorox, C. Hontoria, M. Benito, Appl. Energy 88, 1703 (2011)
M.S. Mecibah, T.E. Boukelia, R. Tahtah, K. Gairaa, Renew. Sustain. Energy Rev. 36, 194 (2014)
S. Mohanty, P.K. Patra, S.S. Sahoo, Renew. Sustain. Energy Rev. 56, 778 (2016)
J.A. Prescott, Trans. R. Soc. South Austr. 64, 114 (1940)
J.L. Chen, G.S. Li, S.J. Wu, Energy Convers. Manag. 75, 311 (2013)
M. Sahin, Y. Kaya, M. Uyar, S. Yildirim, Int. J. Energy Res. 38, 205 (2014)
A.S.S. Dorolvo, J.A. Jervase, A. Al-lawati, Appl. Energy 71, 307 (2002)
M. Benghanem, A. Mellit, Saudi Arabia. Energy 35, 3751 (2010)
O. Senkal, T. Kuleli, Appl. Energy 86, 1222 (2009)
A. Sözen, E. Arcaklioğlu, M. Özalp, Energy Convers. Manag. 45, 3033 (2004)
A. Mellit, M. Benghanem, A. Hadj-Arab, A.A. Guessoum, Sol. Energy 79, 469 (2005)
A. Gani, K. Mohammadi, S. Shamshirband, J. Piri, Theor. Appl. Climatol. 125, 679 (2016)
J. Zeng, W. Qiao, Short-term solar power prediction using an RBF neural network, in Power and Energy Society General Meeting 2011 (IEEE, 2011)
M. Bou-Rabee, S.A. Sulaiman, M. Saad Saleh, S. Marafi, Renew. Sustain. Energy Rev. 72, 434 (2017)
B.B. Ekici, Measurement 50, 255 (2014)
S. Shamshirband, K. Mohammadi, H. Chen, C. Ma, J. Atmos. Sol.-Terr. Phys. 134, 109 (2015)
J.C. Cao, S. Cao, Energy 31, 3435 (2006)
W.A. Rahoma, U.A. Rahoma, A.H. Hassan, J. Comput. Sci. 7, 1605 (2011)
A. Mellit, S.A. Kalogirou, S. Shaari, A. Hadj Arab, Renew. Energy 33, 1570 (2008)
K. Mohammadi, S. Shamshirband, A. Kamsin, P.C. Lai, Zulkefli Mansor, Renew. Sustain. Energy Rev. 63, 423 (2016)
M.R. Yaich, A. Bouhanik, Atlas solaire Algérien (Centre de développement desénergies renouvelables, 2012) www.cder.dz
Guermoui Mawloud et al., Eur. Phys. J. Plus 133, 22 (2018)
A. Rabehi, G. Mawloud, L. Djemoui, Int. J. Ambient Energy (2018) https://doi.org/10.1080/01430750.2018.1443498
L.L.T. Chan, Y. Liu, J. Chen, Ind. Eng. Chem. Res. 52, 18276 (2013)
W. Ni, L. Nørgaard, M. Mørup, Anal. Chim. Acta. 813, 1 (2014)
K. Wang, T. Chen, R. Lau, Chemometr. Intell. Lab. 105, 1 (2011)
Y. Liu, Z. Gao, Appl. Polym. 132, 1 (2015)
A.Y. Sun, D. Wang, X. Xu, J. Hydrol. 511, 72 (2014)
C.E. Rasmussen, C.K.I. Williams, Gaussian Processes For Machine Learning (MIT Press, 2006)
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Guermoui, M., Gairaa, K., Rabehi, A. et al. Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate. Eur. Phys. J. Plus 133, 211 (2018). https://doi.org/10.1140/epjp/i2018-12029-7
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DOI: https://doi.org/10.1140/epjp/i2018-12029-7