457
Views
55
CrossRef citations to date
0
Altmetric
Original Articles

Hybrid models for global solar radiation prediction: a case study

, &
Pages 31-40 | Received 25 Oct 2017, Accepted 03 Feb 2018, Published online: 06 Mar 2018

References

  • Al-Alawi, S. M., and H. A. Al-Hinai. 1998. “An ANN-based Approach for Predicting Global Radiation in Locations with No Direct Measurement Instrumentation.” Renewable Energy 14 (1–4): 199–204. doi: 10.1016/S0960-1481(98)00068-8
  • Alam, S., S. C. Kaushik, and S. N. Garg. 2006. “Computation of Beam Solar Radiation at Normal Incidence Using Artificial Neural Network.” Renewable Energy 31: 1483–1491. doi: 10.1016/j.renene.2005.07.010
  • Al-Hajj, Rami, and Ali Assi. 2017. “ Estimating Solar Irradiance Using Genetic Programming Technique and Meteorological Records.”
  • Al-Shamisi, Maitha H., Ali H. Assi, and Hassan AN Hejase. 2013. “Artificial Neural Networks for Predicting Global Solar Radiation in Al Ain City – UAE.” International Journal of Green Energy 10 (5): 443–456. doi: 10.1080/15435075.2011.641187
  • Almorox, J., and C. Hontoria. 2004. “Global Solar Radiation Estimating Using Sunshine Duration in Spain.” Energy Conversion and Management 45: 1529–1535. doi: 10.1016/j.enconman.2003.08.022
  • Angstrom, A. 1924. “Solar and Terrestrial Radiation. Quarterly Journal of Royal.” Meteorological Society 5 (0): 121–125.
  • Asl, S. F. Z., A. Karami, G. Ashari, A. Assareh, and N. Hedayat. 2011. “Daily Global Solar Radiation Modelling Using Multi-Layer Perceptron (MLP) Neural Networks.” World Academy of Science 79: 740–742.
  • Assi, Ali, Mohammed Jama, and Maitha Al-Shamisi. 2012. “Prediction of Global Solar Radiation in Abu Dhabi.” ISRN Renewable Energy.
  • Behrang, M. A., E. Assareh, A. Ghanbarzadeh, and A. R. Noghrehabadi. 2010. “The Potential of Different Artificial Neural Network (ANN) Techniques in Daily Global Solar Radiation Modeling Based on Meteorological Data.” Solar Energy 84: 1468–1480. doi: 10.1016/j.solener.2010.05.009
  • Benghanem, M., and A. Mellit. 2010. “Radial Basis Function Network-based Prediction of Global Solar Radiation Data: Application for Sizing of a Stand-Alone Photovoltaic System at Al-Madinah.” Saudi Arabia, Energy 35 (9): 3751–3762.
  • Benkaciali, S., M. Haddadi, A. Khellaf, K. Gairaa, and M. Guermoui. 2016. “Evaluation of the Global Solar Irradiation From the Artificial Neural Network Technique.” Revue des Energies Renouvelables 19 (4): 617–631.
  • Gairaa, K., A. Khellaf, Y. Messlem, and F. Chellali. 2016. “Estimation of the Daily Global Solar Radiation Based on Box–Jenkins and ANN Models: A Combined Approach.” Renewable and Sustainable Energy Reviews 57: 238–249. doi: 10.1016/j.rser.2015.12.111
  • Gani, A., K. Mohammadi, S. Shamshirband, H. Khorasanizadeh, A. S. Danesh, J. Piri, … M. Zamani. 2016. “Day of the Year-Based Prediction of Horizontal Global Solar Radiation by a Neural Network Auto-Regressive Model.” Theoretical and Applied Climatology 125 (3–4): 679–689. doi: 10.1007/s00704-015-1533-8
  • Guermoui, M., A. Rabehi, S. Benkaciali, and D. Djafer. 2016. “Daily Global Solar Radiation Modelling Using Multi-Layer Perceptron Neural Networks in Semi-arid Region.” Leonardo Electronic Journal of Practices and Technologies 28: 35–46.
  • Hassanpour Kashani, M. 2008. “Flood Estimation at Ungauged Sites Using a New Hybrid Model.” Journal of Applied Sciences 9: 1744–1749.
  • Hejase, Hassan AN, Maitha H. Al-Shamisi, and Ali H. Assi. 2014. “Modeling of Global Horizontal Irradiance in the United Arab Emirates with Artificial Neural Networks.” Energy 77: 542–552. doi: 10.1016/j.energy.2014.09.064
  • Hejase, Hassan AN, and Ali H. Assi. 2012. “Time-series Regression Model for Prediction of Mean Daily Global Solar Radiation in Al-Ain, UAE.” ISRN Renewable Energy.
  • Kalogirou, S. A. 2000. “Applications of Artificial Neural-networks for Energy Systems.” Applied Energy 67: 17–35. doi: 10.1016/S0306-2619(00)00005-2
  • Khatib, T., A. Mohamed, K. Sopian, and M. Mahmoud. 2012. “Solar Energy Prediction for Malaysia Using Artificial Neural Networks.” International Journal of Photoenergy, 1–16.
  • Koca, A., H. F. Oztop, Y. Varol, and G. O. Koca. 2011. “Estimation of Solar Radiation Using Artificial Neural Networks with Different Input Parameters for Mediterranean Region of Anatolia in Turkey.” Expert Systems with Applications 38: 8756–8762. doi: 10.1016/j.eswa.2011.01.085
  • Kumar, S., and T. Kaur. 2016. “Development of ANN Based Model for Solar Potential Assessment Using Various Meteorological Parameters.” Energy Procedia 90: 587–592. doi: 10.1016/j.egypro.2016.11.227
  • Linares-Rodríguez, A., J. A. Ruiz-Arias, D. Pozo-Vázquez, and J. Tovar-Pescador. 2011. “Generation of Synthetic Daily Global Solar Radiation Data Based on ERA-Interim Reanalysis and Artificial Neural Networks.” Energy 36: 5356–5365. doi: 10.1016/j.energy.2011.06.044
  • Linares-Rodriguez, A., J. A. Ruiz-Arias, D. Pozo-Vazquez, and J. Tovar-Pescador. 2013. “An Artificial Neural Network Ensemble Model for Estimating Global Solar Radiation from Meteosat Satellite Images.” Energy 61: 636–645. doi: 10.1016/j.energy.2013.09.008
  • Mellit, A., M. Benghanem, A. Hadj Arab, and A. Guessoum. 2005. “A Simplified Model for Generating Sequences of Global Radiation Data for Isolated Sites: Using Artificial Neural Network and a Library of Markov Transition Matrices.” Solar Energy 79: 468e82. doi: 10.1016/j.solener.2004.12.006
  • Mellit, A., A. Hadj Arab, N. Khorissi, and H. Salhi. 2007. “An ANFIS-Based Forecasting for Solar Radiation Data from Sunshine Duration and Ambient Temperature.” Power Engineering Society General Meeting. IEEE, 1–6.
  • Moghaddamnia, A., R. Remesan, M. H. Kashani, M. Mohammadi, D. Han, and J. Piri. 2009. “Comparison of LLR, MLP, Elman, NNARX and ANFIS Models—with a Case Study in Solar Radiation Estimation.” Journal of Atmospheric and Solar-Terrestrial Physics 71 (8): 975–982. doi: 10.1016/j.jastp.2009.04.009
  • Mohandes, M., S. Rehman, and T. O. Halawani. 1998. “Estimation of Global Solar Radiation Using Artificial Neural Networks.” Renewable Energy 14 (1–4): 179–184. doi: 10.1016/S0960-1481(98)00065-2
  • Mubiru, J. 2008. “Predicting Total Solar Irradiation Values Using Artificial Neural Networks.” Renewable Energy 33: 2329–2332. doi: 10.1016/j.renene.2008.01.009
  • Rabehi, A., G. Mawloud, D. Djelloul, and Z. Mohamed. 2015. Radial Basis Function Neural Networks Model to Estimate Global Solar Radiation in Semi-Arid Area.” Leonardo Electronic Journal of Practices and Technologies 27: 177–184.
  • Ramedani, Z., M. Omid, and A. Keyhani. 2013. “Modelling Solar Energy Potential in a Tehran Province Using Artificial Neural Networks.” International Journal of Green Energy 10: 427–441. doi: 10.1080/15435075.2011.647172
  • Rumbayan, M., A. Abudureyimu, and K. Nagasaka. 2012. “Mapping of Solar Energy Potential in Indonesia Using Artificial Neural Network and Geographical Information System.” Renewable & Sustainable Energy Reviews 16: 1437–1449. doi: 10.1016/j.rser.2011.11.024
  • Shataeea, S., H. Weinaker, and M. Babanejad. 2011. “Plot-level Forest Volume Estimation Using Airborne Laser Scanner and TM Data, Comparison of Boosting and Random Forest Tree Regression Algorithms.” Procedia Environmental Sciences 7: 68–73. doi: 10.1016/j.proenv.2011.07.013
  • Sözen, A., E. Arcaklioğlu, and M. Özalp. 2004. “Estimation of Solar Potential in Turkey by Artificial Neural net Works Using Meteorological and Geographical Data.” Energy Convers Manag 45: 3033–3052. doi: 10.1016/j.enconman.2003.12.020
  • Sözen, A., E. Arcaklioğlu, M. Özalp, and E. G. Kanit. 2004. “Use of Artificial Neural net Works for Mapping of Solar Potential in Turkey.” Applied Energy 77: 273–286. doi: 10.1016/S0306-2619(03)00137-5
  • Westreich, D., J. Lessler, and M. J. Funk. 2010. “Propensity Score Estimation: Neural Networks, Support Vector Machines, Decision Trees (CART), and Meta-Classifiers as Alternatives to Logistic Regression.” Journal of Clinical Epidemiology 63 (8): 826–833. doi: 10.1016/j.jclinepi.2009.11.020
  • Zheng, G. P. 2003. “Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model.” Neurocomputing 50: 159–175. doi: 10.1016/S0925-2312(01)00702-0
  • Zounemat-Kermani, M., T. Rajaee, A. Ramezani-Charmahineh, and J. F. Adamowski. 2017. “Estimating the Aeration Coefficient and Air Demand in Bottom Outlet Conduits of Dams Using GEP and Decision Tree Methods.” Flow Measurement and Instrumentation 54: 9–19. doi: 10.1016/j.flowmeasinst.2016.11.004

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.