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Research Article

Machine Learning-Based Optimization of Wind-PV Solution for Grid Demand

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Received 04 Oct 2023, Accepted 30 Nov 2023, Published online: 23 Dec 2023

References

  • S. Hoseinzadeh, M. H. Ghasemi and S. Heyns, “Application of hybrid systems in solution of low power generation at hot seasons for micro hydro systems,” Renew. Energ., vol. 160, pp. 323–332, 2020. DOI: 10.1016/j.renene.2020.06.149.
  • E. Yatiyana, S. Rajakaruna and A. Ghosh, “Wind speed and direction forecasting for wind power generation using Arima model,” pp. 1–6, 2017. DOI: 10.1109/AUPEC.2017.8282494.
  • A. Mahmoudan, P. Samadof, S. Hosseinzadeh and D. A. Garcia, “A multigeneration cascade system using ground-source energy with cold recovery: 3E analyses and multi-objective optimization,” Energy, vol. 233, pp. 121185, 2021. DOI: 10.1016/j.energy.2021.121185.
  • J. Kalapala Prasad, et al., “A machine learning-based novel energy optimization algorithm in a photovoltaic solar power system,” Hindawi Int. J. Photoenerg., vol. 2022, pp. 9, 2022. DOI: 10.1155/2022/2845755.
  • G. Xu, Z. Ye and H. Xie, “The current state and future development of wind turbine technology,” Strateg. Study Chin. Acad. Eng., vol. 20, no. 3, pp. 44–50, 2018. DOI: 10.15302/J-SSCAE-2018.03.007.
  • O. Erixno, N. Abd Rahim, F. Ramadhani and N. N. Adzman, “Energy management of renewable energy-based combined heat and power systems: a review,” Sustain. Energ. Tech- Nol. Assess, vol. 51, pp. 101944, 2022. DOI: 10.1016/j.seta.2021.101944.
  • J. Liu, Q. Shi, R. Han and J. Yang, “A Hybrid GA–PSO–CNN model for ultra-short-term wind power forecasting,” Energy, vol. 14, no. 20, pp. 6500, 2021. DOI: 10.3390/en14206500.
  • In Proceedings of the 2017 Australasian Universities Power Engineering Conference (AUPEC), Melbourne, Australia, 19–22 November 2017; Institute of Electrical and Electronics Engineers: Piscataway, NJ, USA, pp. 1–6.
  • S. H. Feng, J. Y. Xu and H. B. Shen, “Articial intelligence in bioinformatics: automated methodology development for protein residue contact map prediction,” Biomedical Information Technology. Amsterdam, The Netherlands: Elsevier, 2020, DOI: 10.1016/B978-0-12-816034-3.00007-9.
  • M. Kuzlu, U. Cali, V. Sharma and O. Guler, “Gaining insight into solar photovoltaic power generation forecasting utilizing explainable articial intelligence tools,” IEEE Access, vol. 8, pp. 187814–187823, 2020. DOI: 10.1109/ACCESS.2020.3031477.
  • A. Rajaram and K. Sathiyaraj, “An improved optimization technique for energy harvesting system with grid connected power for green house management,” J. Electr. Eng. Technol., vol. 17, no. 5, pp. 2937–2949, 2022. DOI: 10.1007/s42835-022-01033-2.
  • P. Ashok Babu, J. L. Mazher Iqbal, S. Siva Priyanka, M. Jithender Reddy, G. Sunil Kumar and A. Rajaram, “Power control and optimization for power loss reduction using deep learning in microgrid systems,” Electr. Power Comp. Sys., pp. 1–14, 2023. DOI: 10.1080/15325008.2023.2217175.
  • H. Shekhar, et al., “Demand side control for energy saving in renewable energy resources using deep learning optimization,” Electr. Power Comp. Sys., vol. 51, no. 19, pp. 2397–2413, 2023. DOI: 10.1080/15325008.2023.2246463.
  • A. F. Tazay, M. M. Samy and S. Barakat, “A techno-economic feasibility analysis of an autonomous hybrid renewable energy sources for university building at Saudi Arabia,” J. Electr. Eng. Technol., vol. 15, no. 6, pp. 2519–2527, 2022. DOI: 10.1007/s42835-020-00539-x.
  • M. B. Eteiba, S. Barakat, M. M. Samy and W. I. Wahba, “Optimization of an off-grid PV/Biomass hybrid system with different battery technologies,” Sustain. cities Societ, vol. 40, pp. 713–727, 2018. DOI: 10.1016/j.scs.2018.01.012.
  • G. Bathla, et al., “Autonomous vehicles and intelligent automation: applications, challenges, and opportunities,” HindawiMob. Inform. Sys., vol. 2022, pp. 36, 2022. DOI: 10.1155/2022/7632892.
  • X. Li, “CNN-GRU model based on attention mechanism for large-scale energy storage optimization in smart grid,” Front. Energy Res, vol. 11, pp. 1228256, 2023. DOI: 10.3389/fenrg.2023.1228256.
  • R. Gregor, M. Lutzeler, M. Pellkofer, K. H. Siedersberger and E. D. Dickmanns, “EMS-vision: a perceptual system for autonomous vehicles,” IEEE Trans. Intell. Transport. Syst., vol. 3, no. 1, pp. 48–59, 2002. DOI: 10.1109/6979.994795.
  • C. M. Martinez, X. Hu, D. Cao, E. Velenis, B. Gao and M. Wellers, “Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective,” IEEE Trans. Veh. Technol., vol. 66, no. 6, pp. 4534–4549, 2017. DOI: 10.1109/TVT.2016.2582721.
  • H. A. Maddah, M. Bassyouni, M. H. Abdel-Aziz, M. S. Zor Omba and A. F. Al-Hossainy, “Performance estimation of a mini-passive solar still _via_ machine learning,” Renew. Energ., vol. 162, pp. 489–503, 2020. DOI: 10.1016/j.renene.2020.08.006.
  • B. D. Dimd, S. Völler, U. Cali, and O.-M. Midtgård. A review of machine learning-based photovoltaic output power forecasting: nordic context. IEEE Access. vol. 10, pp. 26404-26425, 2022. DOI: 10.1109/ACCESS.2022.3156942.
  • W. Bendali, I. Saber, B. Bourachdi, M. Boussetta and Y. Mourad, “Deep learning using genetic algorithm optimization for short term solar irradiance forecasting,” Int. Conf. Intell. Comput. Data Sci, pp. 1-8, 2020. DOI: 10.1109/ICDS50568.2020.9268682.
  • P. W. Khan and Y.-C. Byun, “Genetic algorithm based optimized feature engineering and hybrid machine learning for effective energy consumption prediction,” IEEE Access, vol. 8, pp. 196274–196286, 2020. DOI: 10.1109/ACCESS.2020.3034101.
  • M. N. Akhter, S. Mekhilef, H. Mokhlis and N. M. Shah, “Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques,” IET Renew. Power Gener., vol. 13, no. 7, pp. 1009–1023, 2019. DOI: 10.1049/iet-rpg.2018.5649.
  • Z. Liu, et al., “Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: challenges and future perspectives,” Energy, vol. 10, pp. 100195, 2022. DOI: 10.1016/j.egyai.2022.100195.
  • M. Morshed Alam, M. Habibur Rahman, M. Faisal Ahmed, M. Z. Chowdhury and Y. M. Jang, “Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home microgrid system,” Sci. Rep., vol. 12, no. 1, pp. 15133, 2022. DOI: 10.1038/s41598-022-19147-y.
  • H. Shareef, M. S. Ahmed, A. Mohamed and E. Al Hassan, “Review on home energy management system considering demand responses, smart technologies, and intelligent controllers,” IEEE Access, vol. 6, pp. 24498–24509, 2018. DOI: 10.1109/ACCESS.2018.2831917.
  • Y. Sawle, S. C. Gupta and A. Kumar Bohre, “PV- wind hybrid system: a review with case study,” Cog. Eng., vol. 3, no. 1, pp. 1189305, 2016. DOI: 10.1080/23311916.2016.1189305.
  • I. Robu, et al., “Artificial intelligence and machine learning approaches to energy demand-side response: a systematic review,” Renew. Sustain. Energ. Rev., vol. 130, pp. 109899, 2020. DOI: 10.1016/j.rser.2020.109899.
  • N. A. C. Atienza, J. R. A. T. Jao, J. A. D. S. Angeles, E. L. T. Singzon and D. D. Acula, “Prediction and visualization of electricity consumption in the Philippines using articial neural networks, particle swarm optimization, and autoregressive integrated moving average,” Proc. 3rd Int. Conf. Comput. Commun. Syst., pp. 135138, 2018. DOI: 10.1109/CCOMS.2018.8463351.

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