Estimates of hydroelectric energy generation in BRICS-T countries using a new hybrid model

References

• Barykina, Y. N., A. G. Chernykh, and B. Na. 2022. Energy production as a basis for sustainable development in the BRICS countries. IOP Conference Series: Earth and Environmental Science 990: 012016. doi:10.1088/1755-1315/990/1/012016.
   Google Scholar
• Bildirci, M. E., and T. Bakirtas. 2014. The relationship among oil, natural gas and coal consumption and economic growth in BRICTS (Brazil, Russian, India, China, Turkey and South Africa) countries. Energy 65:134–25. doi:10.1016/j.energy.2013.12.006.
   Web of Science ®Google Scholar
• Ceylan, H., and H. K. Ozturk. 2004. Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management 45:2525–37. doi:10.1016/j.enconman.2003.11.010.
   Web of Science ®Google Scholar
• Chong, K. L., S. H. Lai, A. N. Ahmed, W. Z. W. Jaafar, R. V. Rao, M. Sherif, A. Sefelnasr, and A. El-Shafie. 2021. Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms. Institute of Electrical and Electronics Engineers Access 9:19488–505. doi:10.1109/ACCESS.2021.30544.24.
   Google Scholar
• Cinar, D., G. Kayakutlu, and T. Daim. 2010. Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey. Energy 35 (4):1724–29. doi:10.1016/j.energy.2009.12.025.
   Web of Science ®Google Scholar
• Dede, T., B. Atmaca, M. Grzywinski, and R. V. Rao. 2022. Optimal design of dome structures with recently developed algorithm: Rao series. Structures 42:65–79. doi:10.1016/j.istruc.2022.06.010.
   Web of Science ®Google Scholar
• Duran, M. S. 2020. Determinants of energy consumption in BRICS-T countries: An econometric application. Ph.D. Thesis, Necmettin Erbakan University Institute of Social Sciences, Konya, Turkey; 2020 [in Turkish], Retrieved March 2023, from https://acikerisim.erbakan.edu.tr/xmlui/bitstream/handle/20.500.12452/4966/633199.pdf?sequence=1&isAllowed=y
   Google Scholar
• Ehteram, M., F. B. Banadkooki, C. M. Fai, M. Moslemzadeh, M. Sapitang, A. N. Ahmed, D. Irwan, and A. El-Shafie. 2021. Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy. Energy Reports 7:3703–25. doi:10.1016/j.egyr.2021.06.008.
   Web of Science ®Google Scholar
• Ehteram, M., S. B. Koting, H. A. Afan, N. S. Mohd, M. A. Malek, A. N. Ahmed, A. H. El-Shafie, C. C. Onn, S. H. Lai, and A. El-Shafie. 2019. New evolutionary algorithm for optimizing hydropower generation considering multireservoir systems. Applied Sciences 9 (11):2280. doi:10.3390/app9112280.
   Google Scholar
• General Directorate of State Hydraulics Works (DSI). 2023. Retrieved March 2023, from https://www.dsi.gov.tr/Sayfa/Detay/1621
   Google Scholar
• Hanoon, M. S., A. N. Ahmed, A. Razzaq, A. Y. Oudah, A. Alkhayyat, Y. F. Huang, A. El-Shafie, and A. El-Shafie. 2023. Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China. Ain Shams Engineering Journal 14 (4):101919. doi:10.1016/j.asej.2022.101919.
   Web of Science ®Google Scholar
• Hydropower special market report analysis and forecast to 2030. 2023. Published by International Energy Agency. Retrieved March 2023, from https://iea.blob.core.windows.net/assets/4d2d4365-08c6-4171-9ea2-8549fabd1c8d/HydropowerSpecialMarketReport_corr.pdf
   Google Scholar
• International Monetary Fund (IMF). 2023. Retrieved March 2023, from https://www.imf.org/en/Data.
   Google Scholar
• Jana, S. K. 2022. Sustainable energy development in emerging economies: A study on BRICS. In Environmental sustainability, growth trajectory and gender: Contemporary issues of developing economies, Vol. UK, 23–35. Bingley: Emerald Publishing Limited. doi:10.1108/978-1-80262-153-220221002.
   Google Scholar
• Karakurt, I. 2021. Modelling and forecasting the oil consumptions of the BRICS-T countries. Energy 220:119720. doi:10.1016/j.energy.2020.119720.
   Web of Science ®Google Scholar
• Karumanchi, H., and S. Mathew. 2022. Forecasting of hydropower generation of India using autoregressive integrated moving average model. Journal of Algebraic Statistics 13:3124–28.
   Google Scholar
• Kavaklioglu, K., H. Ceylan, H. K. Ozturk, and O. E. Canyurt. 2009. Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management 50 (11):2719–27. doi:10.1016/j.enconman.2009.06.016.
   Web of Science ®Google Scholar
• Khan, A. M., and M. Osinska. 2021. How to predict energy consumption in BRICS countries? Energies 14 (10):2749. doi:10.3390/en14102749.
   Web of Science ®Google Scholar
• Khan, A. M., and M. Osińska. 2023. Comparing forecasting accuracy of selected grey and time series models based on energy consumption in Brazil and India. Expert Systems with Applications 212:118840. doi:10.1016/j.eswa.2022.118840.
   Google Scholar
• Khan, M., Z. Ullah, O. Masek, S. R. Naqvi, and M. N. A. Khan. 2022. Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms. Bioresource Technology 355:127215. doi:10.1016/j.biortech.2022.127215.
   PubMedGoogle Scholar
• Kıran, M. S., E. Özceylan, M. Gündüz, and T. Paksoy. 2012. A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Conversion and Management 53 (1):75–83. doi:10.1016/j.enconman.2011.08.004.
   Web of Science ®Google Scholar
• Kisi, O., C. Ozkan, and B. Akay. 2012. Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. Canadian Journal of Fisheries and Aquatic Sciences 428:94–103. doi:10.1016/j.jhydrol.2012.01.026.
   Google Scholar
• Magazzino, C. 2023. Ecological footprint, electricity consumption, and economic growth in China: Geopolitical risk and natural resources governance. Empirical Economics 66 (1):1–25. doi:10.1007/s00181-023-02460-4.
   Web of Science ®Google Scholar
• Magazzino, C., and M. Mele. 2022. A new machine learning algorithm to explore the CO2 emissions-energy use-economic growth trilemma. Annals of Operations Research. doi:10.1007/s10479-022-04787-0.
   Web of Science ®Google Scholar
• Magazzino, C., M. Mele, C. Drago, S. Kuşkaya, C. Pozzi, and U. Monarca. 2023. The trilemma among CO2 emissions, energy use, and economic growth in Russia. Scientific Reports 13 (1):10225. doi:10.1038/s41598-023-37251-5.
   PubMed Web of Science ®Google Scholar
• Malhan, P., and M. Mittal. 2022. A novel ensemble model for long-term forecasting of wind and hydro power generation. Energy Conversion and Management 251:114983. doi:10.1016/j.enconman.2021.114983.
   Web of Science ®Google Scholar
• Najah, A., A. El-Shafie, O. A. Karim, and O. Jaafar. 2011. Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations. Hydrology and Earth System Sciences 15 (8):2693–708. doi:10.5194/hess-15-2693-2011.
   Web of Science ®Google Scholar
• Nayak, S. K., S. C. Nayak, and S. Das. 2022. Modeling and forecasting cryptocurrency closing prices with rao algorithm-based artificial neural networks: A machine learning approach. Fin Tech 1 (1):47–62. doi:10.3390/fintech1010004.
   Google Scholar
• Ojekemi, O. S., M. Ağa, and C. Magazzino. 2023. Towards achieving sustainability in the BRICS economies: The role of renewable energy consumption and economic risk. Energies 16 (14):5287. doi:10.3390/en16145287.
   Web of Science ®Google Scholar
• Oliveria, V. A., C. R. Mello, M. R. Viola, and R. Srinivasan. 2017. Assessment of climate change impacts on streamflow and hydropower potential in the headwater region of the Grande river basin, Southeastern Brazil. International Journal of Climatology 37 (15):5005–23. doi:10.1002/joc.5138.
   Web of Science ®Google Scholar
• Pandey, A. K., P. K. Singh, M. Nawaz, and A. K. Kushwaha. 2023. Forecasting of non-renewable and renewable energy production in India using optimized discrete grey model. Environmental Science and Pollution Research 30 (3):8188–206. doi:10.1007/s11356-022-22739-w.
   PubMed Web of Science ®Google Scholar
• Rao, R. V. 2020. Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems. International Journal of Industrial Engineering Computations 11:107–30. doi:10.5267/j.ijiec.2019.6.002.
   Web of Science ®Google Scholar
• Rao, R. V., and R. B. Pawar. 2020. Constrained design optimization of selected mechanical system components using Rao algorithms. Applied Soft Computing Journal 89:106141. doi:10.1016/j.asoc.2020.106141.
   Web of Science ®Google Scholar
• Rumelhart, D. E., G. E. Hinton, and R. J. Williams. 1986. Learning representations by back-propagating errors. Nature 323 (6088):533–36. doi:10.1038/323533a0.
   Web of Science ®Google Scholar
• Soito, J. L. S., and M. A. V. Freitas. 2011. Amazon and the expansion of hydropower in Brazil: Vulnerability, impacts and possibilities for adaptation to global climate change. Renewable and Sustainable Energy Reviews 15 (6):3165–77. doi:10.1016/j.rser.2011.04.006.
   Web of Science ®Google Scholar
• Sperling, E. V. 2012. Hydropower in Brazil: Overview of positive and negative environmental aspects. Energy Procedia 18:110–18. doi:10.1016/j.egypro.2012.05.023.
   Google Scholar
• Statistical review of world energy 2021. Published by British Petroleum. Retrieved March 2023, from https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf
   Google Scholar
• Turkish Statistical Institue (TURKSTAT). Retrieved March 2023, from https://data.tuik.gov.tr/Kategori/GetKategori?p=ulusal-hesaplar-113&dil=2.
   Google Scholar
• Uzlu, E. 2019. Application of jaya algorithm-trained artificial neural networks for prediction of energy use in the nation of Turkey, 2019. Energy Sources, Part B: Economics, Planning, & Policy 14 (5):183–200. doi:10.1080/15567249.2019.1653405.
   Web of Science ®Google Scholar
• Uzlu, E. 2021. Estimates of hydroelectric energy generation in Turkey with jaya algorithm-optimized artificial neural networks. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji 9 (3):446–62. doi:10.29109/gujsc.910228.
   Google Scholar
• Uzlu, E., A. Akpınar, H. T. Öztürk, S. Nacar, and M. Kankal. 2014. Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey. Energy 69:638–47. doi:10.1016/j.energy.2014.03.059.
   Web of Science ®Google Scholar
• Wang, Z. X., Q. Li, and L. L. Pei. 2017. Grey forecasting method of quarterly hydropower production in China based on a data grouping approach. Applied Mathematical Modelling 51:302–16. doi:10.1016/j.apm.2017.07.003.
   Web of Science ®Google Scholar
• Wang, L., Z. Wang, H. Liang, and C. Huang. 2020. Parameter estimation of photovoltaic cell model with rao-1 algorithm. Optik-International Journal for Light and Electron Optics 210:163846. doi:10.1016/j.ijleo.2019.163846.
   Google Scholar
• Wang, S., L. Yu, L. Tang, and S. Wang. 2011. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy 36 (11):6542–54. doi:10.1016/j.energy.2011.09.010.
   Web of Science ®Google Scholar
• World Bank indicators (WBI). 2023. Retrieved March 2023, from https://data.worldbank.org/indicator/SP.POP.TOTL?end=2021&locations=ZA&start=1990
   Google Scholar
• Yang, M., C. Magazzino, A. A. Awosusi, and N. Abdulloev. 2023. Determinants of load capacity factor in BRICS countries: A panel data analysis. Natural Resources Forum 125 (3):189–99. doi:10.1111/1477-8947.12331.
   Google Scholar
• Yılmaz, M., and T. Dede. 2023. Multi-objective time–cost trade-off optimization for the construction scheduling with Rao algorithms. Structures 48:798–808. doi:10.1016/j.istruc.2023.01.006.
   Web of Science ®Google Scholar
• Zeng, B., C. He, C. Mao, and Y. Wu. 2023. Forecasting China’s hydropower generation capacity using a novel grey combination optimization model. Energy 262:125341. doi:10.1016/j.energy.2022.125341.
   Web of Science ®Google Scholar