Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting

References

• Abdel-Nasser, M., and K. Mahmoud. 2019. Accurate photovoltaic power forecasting models using deep lstm-rnn. Neural Computing & Applications 31 (7):2727–334. doi:10.1007/s00521-017-3225-z.
   Web of Science ®Google Scholar
• Ajayi, O., O. Ohijeagbon, C. Nwadialo, and O. Olasope. 2014. New model to estimate daily global solar radiation over nigeria. Sustainable Energy Technologies and Assessments 5:28–36. doi:10.1016/j.seta.2013.11.001.
   Google Scholar
• Allen, R. G., M. Tasumi, A. Morse, and R. Trezza. 2005. A Landsat-based energy balance and evapotranspiration model in western us water rights regulation and planning. Irrigation and Drainage Systems 19 (3):251–68. doi:10.1007/s10795-005-5187-z.
   Google Scholar
• Antonopoulos, V. Z., D. M. Papamichail, V. G. Aschonitis, and A. V. Antonopoulos 2019. Solar radiation estimation methods using ann and empirical models. Computers and Electronics in Agriculture 160, 160–67.
   Web of Science ®Google Scholar
• Azimi, R., M. Ghayekhloo, and M. Ghofrani. 2016. A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting. Energy Conversion and Management 118:331–44. doi:10.1016/j.enconman.2016.04.009.
   Web of Science ®Google Scholar
• Bansal, A. K., and V. Garg (2021). Biogeography-based optimization (bbo) trained neural networks for wind speed forecasting. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering, Mahendergarh, ed. P. Singh, R. K. Gupta, K. Ray, and A. Bandyopadhyay, Vol. 1169, pp. 79–94. Singapore: Springer. doi:10.1007/978-981-15-5414-8_6.
   Google Scholar
• Benghanem, M., A. Mellit, and S. Alamri. 2009. Ann-based modelling and estimation of daily global solar radiation data: A case study. Energy Conversion and Management 50 (7):1644–55. doi:10.1016/j.enconman.2009.03.035.
   Web of Science ®Google Scholar
• Benmouiza, K., and A. Cheknane. 2019. Clustered anfis network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theoretical and Applied Climatology 137 (1):31–43. doi:10.1007/s00704-018-2576-4.
   Web of Science ®Google Scholar
• Chaouachi, A., R. M. Kamel, and K. Nagasaka. 2010. Neural network ensemble-based solar power generation short-term forecasting. Journal of Advanced Computational Intelligence and Intelligent Informatics 14 (1):69–75. doi:10.20965/jaciii.2010.p0069.
   Google Scholar
• David, M., F. Ramahatana, P. -J. Trombe, and P. Lauret. 2016. Probabilistic forecasting of the solar irradiance with recursive arma and garch models. Solar Energy 133:55–72. doi:10.1016/j.solener.2016.03.064.
   Web of Science ®Google Scholar
• Dinpashoh, Y., S. Jahanbakhsh-Asl, A. Rasouli, M. Foroughi, and V. Singh. 2019. Impact of climate change on potential evapotranspiration (case study: West and nw of Iran). Theoretical and Applied Climatology 136 (1):185–201. doi:10.1007/s00704-018-2462-0.
   Web of Science ®Google Scholar
• Feng, L., A. Lin, L. Wang, W. Qin, and W. Gong. 2018. Evaluation of sunshine-based models for predicting diffuse solar radiation in china. Renewable and Sustainable Energy Reviews 94:168–82. doi:10.1016/j.rser.2018.06.009.
   Web of Science ®Google Scholar
• Ghimire, S., R. C. Deo, N. Raj, and J. Mi. 2019. Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Applied Energy 253:113541. doi:10.1016/j.apenergy.2019.113541.
   Web of Science ®Google Scholar
• Guermoui, M., F. Melgani, K. Gairaa, and M. L. Mekhalfi. 2020. A comprehensive review of hybrid models for solar radiation forecasting. Journal of Cleaner Production 258:120357. doi:10.1016/j.jclepro.2020.120357.
   Web of Science ®Google Scholar
• Hejase, H. A., M. H. Al-Shamisi, and A. H. Assi. 2014. Modeling of global horizontal irradiance in the united Arab emirates with artificial neural networks. Energy 77:542–52. doi:10.1016/j.energy.2014.09.064.
   Web of Science ®Google Scholar
• Huang, J., M. Korolkiewicz, M. Agrawal, and J. Boland. 2013. Forecasting solar radiation on an hourly time scale using a coupled autoregressive and dynamical system (cards) model. Solar Energy 87:136–49. doi:10.1016/j.solener.2012.10.012.
   Web of Science ®Google Scholar
• Jiang, Y. 2008. Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models. Energy Policy 36 (10):3833–37. doi:10.1016/j.enpol.2008.06.030.
   Web of Science ®Google Scholar
• Khan, K., and A. Sahai. 2012. A comparison of ba, ga, pso, bp and lm for training feed forward neural networks in e-learning context. International Journal of Intelligent Systems & Applications 4 (7):23. doi:10.5815/ijisa.2012.07.03.
   Google Scholar
• Khare, V., S. Nema, and P. Baredar. 2016. Solar–wind hybrid renewable energy system: A review. Renewable and Sustainable Energy Reviews 58:23–33. doi:10.1016/j.rser.2015.12.223.
   Web of Science ®Google Scholar
• López, G., F. Batlles, and J. Tovar-Pescador. 2005. Selection of input parameters to model direct solar irradiance by using artificial neural networks. Energy 30 (9):1675–84. doi:10.1016/j.energy.2004.04.035.
   Web of Science ®Google Scholar
• Mandal, K., B. Bhattacharya, B. Tudu, and N. Chakraborty (2011). A novel population-based optimization algorithm for optimal distribution capacitor planning. In 2011 International Conference on Energy, Automation and Signal, Bhubaneswar, India, pp. 1–6. IEEE. doi:10.1109/ICEAS.2011.6147075.
   Google Scholar
• Marquez, R., and C. F. Coimbra. 2011. Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the nws database. Solar Energy 85 (5):746–56. doi:10.1016/j.solener.2011.01.007.
   Web of Science ®Google Scholar
• Mohanty, S., P. K. Patra, and S. S. Sahoo. 2016. Prediction and application of solar radiation with soft computing over traditional and conventional approach–a comprehensive review. Renewable and Sustainable Energy Reviews 56:778–96. doi:10.1016/j.rser.2015.11.078.
   Web of Science ®Google Scholar
• Ozgoren, M., M. Bilgili, and B. Sahin. 2012. Estimation of global solar radiation using ann over turkey. Expert Systems with Applications 39 (5):5043–51. doi:10.1016/j.eswa.2011.11.036.
   Web of Science ®Google Scholar
• Peled, A., and J. Appelbaum. 2013. Evaluation of solar radiation properties by statistical tools and wavelet analysis. Renewable Energy 59:30–38. doi:10.1016/j.renene.2013.03.019.
   Web of Science ®Google Scholar
• Pinker, R., R. Frouin, and Z. Li. 1995. A review of satellite methods to derive surface shortwave irradiance. Remote Sensing of Environment 51 (1):108–24. doi:10.1016/0034-4257(94)00069-Y.
   Web of Science ®Google Scholar
• Premalatha, N., and A. Valan Arasu. 2016. Prediction of solar radiation for solar systems by using ann models with different back propagation algorithms. Journal of Applied Research and Technology 14 (3):206–14. doi:10.1016/j.jart.2016.05.001.
   Google Scholar
• Prescott, J. 1940. Evaporation from a water surface in relation to solar radiation. Transactions of the Royal Society of South Australia 46:114–18.
   Google Scholar
• Rahmati, S. H. A., and M. Zandieh. 2012. A new biogeography-based optimization (bbo) algorithm for the flexible job shop scheduling problem. International Journal of Advanced Manufacturing Technology 58 (9):1115–29. doi:10.1007/s00170-011-3437-9.
   Web of Science ®Google Scholar
• Renno, C., F. Petito, and A. Gatto. 2016. Ann model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building. Journal of Cleaner Production 135:1298–316. doi:10.1016/j.jclepro.2016.07.049.
   Web of Science ®Google Scholar
• Rensheng, C., E. Kang, Y. Jianping, L. Shihua, and Z. Wenzhi. 2004. Validation of five global radiation models with measured daily data in china. Energy Conversion and Management 45 45:1759–69. doi:10.1016/j.enconman.2003.09.019.
   Web of Science ®Google Scholar
• Ren, Y., P. Suganthan, and N. Srikanth. 2015. Ensemble methods for wind and solar power forecasting—a state-of-the-art review. Renewable and Sustainable Energy Reviews 50:82–91. doi:10.1016/j.rser.2015.04.081.
   Web of Science ®Google Scholar
• Samani, Z., A. S. Bawazir, M. Bleiweiss, R. Skaggs, and V. D. Tran. 2007. Estimating daily net radiation over vegetation canopy through remote sensing and climatic data. Journal of Irrigation and Drainage Engineering 133 (4):291–97. doi:10.1061/(ASCE)0733-9437(2007)133:4(291).
   Web of Science ®Google Scholar
• Sfetsos, A., and A. Coonick. 2000. Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy 68 (2):169–78. doi:10.1016/S0038-092X(99)00064-X.
   Web of Science ®Google Scholar
• Shamshirband, S., K. Mohammadi, H. -L. Chen, G. N. Samy, D. Petković, and C. Ma. 2015. Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for iran. Journal of Atmospheric and Solar-Terrestrial Physics 134:109–17. doi:10.1016/j.jastp.2015.09.014.
   Web of Science ®Google Scholar
• Shamshirband, S., K. Mohammadi, J. Piri, D. Petković, and A. Karim. 2016. Hybrid auto-regressive neural network model for estimating global solar radiation in Bandar Abbas, iran. Environmental Earth Sciences 75 (2):1–12. doi:10.1007/s12665-015-4970-x.
   Web of Science ®Google Scholar
• Sharma, V., D. Yang, W. Walsh, and T. Reindl. 2016. Short term solar irradiance forecasting using a mixed wavelet neural network. Renewable Energy 90:481–92. doi:10.1016/j.renene.2016.01.020.
   Web of Science ®Google Scholar
• Simon, D. 2008. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation 12 (6):702–13. doi:10.1109/TEVC.2008.919004.
   Web of Science ®Google Scholar
• Singh, U., and T. S. Kamal. 2012. Concentric circular antenna array synthesis using biogeography based optimization. Majlesi Journal of Electrical Engineering 6 (7):822. doi:10.1049/iet-map.2011.0484.
   Google Scholar
• Srivastava, S., and S. Lessmann. 2018. A comparative study of lstm neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Solar Energy 162:232–47. doi:10.1016/j.solener.2018.01.005.
   Web of Science ®Google Scholar
• Voyant, C., G. Notton, S. Kalogirou, M. -L. Nivet, C. Paoli, F. Motte, and A. Fouilloy. 2017. Machine learning methods for solar radiation forecasting: A review. Renewable Energy 105:569–82. doi:10.1016/j.renene.2016.12.095.
   Web of Science ®Google Scholar
• Wu, G., Y. Liu, and T. Wang. 2007. Methods and strategy for modeling daily global solar radiation with measured meteorological data–a case study in Nanchang station, china. Energy Conversion and Management 48 (9):2447–52. doi:10.1016/j.enconman.2007.04.011.
   Web of Science ®Google Scholar
• Yadav, A. K., and S. Chandel. 2014. Solar radiation prediction using artificial neural network techniques: A review. Renewable and Sustainable Energy Reviews 33:772–81. doi:10.1016/j.rser.2013.08.055.
   Web of Science ®Google Scholar
• Yaghini, M., M. M. Khoshraftar, and M. Fallahi. 2013. A hybrid algorithm for artificial neural network training. Engineering Applications of Artificial Intelligence 26 (1):293–301. doi:10.1016/j.engappai.2012.01.023.
   Web of Science ®Google Scholar
• Zhao, N., X. Zeng, and S. Han. 2013. Solar radiation estimation using sunshine hour and air pollution index in china. Energy Conversion and Management 76:846–51. doi:10.1016/j.enconman.2013.08.037.
   Web of Science ®Google Scholar