1. Sueyoshi, T. and Goto, M., "Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis", Energy Economics, Vol. 42, (2014), 271-288. (DOI: 10.1016/j.eneco.2014.01.004).
2. Zhao, H.-X. and Magoulès, F., "A review on the prediction of building energy consumption", Renewable and Sustainable Energy Reviews, Vol. 16, No. 6, (2012), 3586-3592. (DOI :10.1016/j.rser.2012.02.049).
3. Islam, S.M., Kabir, M.M. and Kabir, N., "Artificial neural networks based prediction of insolation on horizontal surfaces for Bangladesh", CIMTA: Procedia Technology, Vol. 10, (2013),482-4914. (DOI:10.1016/j.protcy.2013.12.386).
4. Kalogirou, S. and Sencan, A., Artificial intelligence techniques in solar energy applications, solar collectors and panels, theory and applications, Dr. Reccab Manyala Ed., ISBN: 978-953-307-142-8, InTech, (2010). (DOI: 10.5772/10343).
5. Tymvios, F.S., Jacovides, C.P., Michaelides, S.C. and Scouteli, C., "Comparative study of Angström’s and artificial neural networks’ methodologies in estimating global solar radiation", Solar Energy, Vol. 78, (2005), 752–762. (DOI: 10.1016/j.solener.2004.09.007).
6. Hernandez Neto, A. and Sanzovo Fiorelli, F.A., "Comparison between detailed model simulation and artificial neural network for forcasting building energy comsuption", Energy and Buildings, Vol. 40, (2008), 2169-2176. (DOI:10.1016 /j.enbuild.2008.06.013).
7. Neelamegama, P. and Amirtham, V.A., "Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms", Journal of Applied Research and Technology, Vol. 14 ,(2016), 206–214. (DOI:10.1016/ j.jart.2016.05.001).
8. Egeonu, D.I., Njoku, H.O., Okolo, P.N. and Enibe, S.O., "Comparative assessment of temperature based ANN and angstrom type models for predicting global solar radiation", Proceedings of Afro-European Conf. for Ind. Advancement, Advances in Intelligent Systems and Computing, (2015), 334. (DOI: 10.1007/978-3-319-13572-4_9. DOI:10.1007/978-3-319-13572-4_9).
9. Li, K., Su, H. and Chu, J., "Forecasting building energy consumption using neural network and hybrid neuro-fuzzy system: A comperative study", Energy and Buildings, Vol. 43, No. 10, (2011), 2893-2899. (DOI:10.1016/j.enbuild. 2011.07.010).
10. Karatasou, S., Santamouris, M. and Geros, V., "Modeling and predicting building’s energy use with artificial neural networks: Methods and results", Energy and Buildings, Vol. 38, No. 8, (2006), 949–958. (DOI: 10.1016/j.enbuild.2005.11.005).
11. Kumar, A.Y. and Chandel, S.S., "Solar radiation prediction using artificial neural network tecniques: A review", Renewable and Sustainable Energy Review, Vol. 33, (2014), 772-781. (DOI;10.1016/j.rser.2013.08.055).
12. Edalati, S., Ameri, M. and Iranmanesh, M., "Estimating and modeling monthly mean daily global solar radiation on horizontal surfaces using artificial neural networks in south east of Iran", Journal of Renewable Energy and Environment (JREE), Vol. 2, No. 1, (Winter 2015), 36-42.
13. Jovanovic, R.Z., Sretenovic, A.A. and Zivkovic, B.D., "Ensemble of various neural networks for prediction of heating energy consumption", Energy and Buildings, Vol. 94, (2015) 189-199. (DOI:10.1016/j.enbuild.2015.02.052).
14. Yohanna, J.K., Itodo, I.N. and Umogbai, V.I., "A model for determining the global solar radiation for Makurdi, Nigeria", Renewable Energy, Vol. 36, (2011), 1989-1992.
15. Yadav, A.K. and Hasmat, M., "Comparison of different artificial neural network techniques in prediction of solar radiation for power generation using different combinations of meterological variables", Proceedings of IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), (2014). (DOI: 10.1109/PEDES.2014.7042063).
16. Renno, C., Petito, F. and Gatto, A., "Artificial neural network models for predicting the solar radiation", Energy Conversion and Management, Vol. 106, (2015), 999-1012. (DOI:10.1016/ j.enconman.2015.10.033).
17. Huang, Y., "Advances in artificial neural networks: Methodological development and application", Algorithms, Vol. 2, (2009), 973-1007. (DOI:10.3390/algor2030973).