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Basiratpour A H, Safieddin Ardabili S M, Zaki Dizji H. An overview of the application of artificial intelligence in renewable energy management. alkhass 2024; 6 (4) :1-8
URL: http://alkhass.srpub.org/article-4-279-en.html
URL: http://alkhass.srpub.org/article-4-279-en.html
1- Department of Agriculture, Shahid Chamran Ahvaz University, Ahvaz, Iran.
2- Associate Professor, Department of Biosystems Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. ,m.safieddin@scu.ac.ir
3- Associate Professor, Department of Biosystems Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2- Associate Professor, Department of Biosystems Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran. ,
3- Associate Professor, Department of Biosystems Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Abstract: (77 Views)
The integration of artificial intelligence (AI) in sustainable and renewable energy systems has become a pivotal driver for enhancing productivity, reducing costs, and overcoming complex challenges. This review aims to consolidate recent advancements in AI applications within renewable energy technologies and systems. By analyzing research reports, this study explores diverse AI approaches across various renewable energy sources, including solar power, photovoltaics, microgrid integration, energy storage, wind energy, and geothermal energy. The review discusses current technological advancements, key methodologies, challenges, and achievements in the field, emphasizing the role of AI in optimizing different facets of renewable energy systems. It also highlights potential challenges and their solutions, along with expected advancements and future trends, providing valuable insights for researchers and engineers in the sustainable energy sector.
Type of Study: Review |
Subject:
Ecosystem Management, Monitoring, Policy and Law
Received: 2024/09/7 | Revised: 2024/10/13 | Accepted: 2024/11/17 | Published: 2024/12/28
Received: 2024/09/7 | Revised: 2024/10/13 | Accepted: 2024/11/17 | Published: 2024/12/28
References
1. Bennagi, A., AlHousrya, O., Cotfas, D. T., & Cotfas, P. A. (2024). Comprehensive study of the artificial intelligence applied in renewable energy. Energy Strategy Reviews, 54, 101446. [DOI:10.1016/j.esr.2024.101446]
2. Ukoba, K., Olatunji, K. O., Adeoye, E., Jen, T. C., & Madyira, D. M. (2024). Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment, 35(7), 3833-3879. [DOI:10.1177/0958305X241256293]
3. Mahapatra, S., Kumar, D., Singh, B., & Sachan, P. K. (2021). Biofuels and their sources of production: A review on cleaner sustainable alternative against conventional fuel, in the framework of the food and energy nexus. Energy Nexus, 4, 100036. [DOI:10.1016/j.nexus.2021.100036]
4. Hadjipaschalis, I., Poullikkas, A., & Efthimiou, V. (2009). Overview of current and future energy storage technologies for electric power applications. Renewable and sustainable energy reviews, 13(6-7), 1513-1522. [DOI:10.1016/j.rser.2008.09.028]
5. Kumar, A., Nagar, S., & Anand, S. (2021). Climate change and existential threats. In Global climate change (pp. 1-31). Elsevier.
https://doi.org/10.1016/B978-0-12-822928-6.00005-8 [DOI:10.1016/B978-0-12-822928-6.00002-2]
6. Energy, S. P. (2014). Technology roadmap. Technical Report, IEA.
7. Deshmukh, M. K. G., Sameeroddin, M., Abdul, D., & Sattar, M. A. (2023). Renewable energy in the 21st century: A review. Materials Today: Proceedings, 80, 1756-1759. [DOI:10.1016/j.matpr.2021.05.501]
8. Sabev, E., Trifonov, R., Pavlova, G., & Rainova, K. (2021, September). Cybersecurity analysis of wind farm SCADA systems. In 2021 International Conference on Information Technologies (InfoTech) (pp. 1-5). IEEE. [DOI:10.1109/InfoTech52438.2021.9548589]
9. Sayed, E. T., Wilberforce, T., Elsaid, K., Rabaia, M. K. H., Abdelkareem, M. A., Chae, K. J., & Olabi, A. G. (2021). A critical review on environmental impacts of renewable energy systems and mitigation strategies: Wind, hydro, biomass and geothermal. Science of the total environment, 766, 144505. [DOI:10.1016/j.scitotenv.2020.144505] [PMID]
10. Hussain, A., Arif, S. M., & Aslam, M. (2017). Emerging renewable and sustainable energy technologies: State of the art. Renewable and sustainable energy reviews, 71, 12-28. [DOI:10.1016/j.rser.2016.12.033]
11. Sonar, D. (2021). Renewable energy based trigeneration systems-technologies, challenges and opportunities. Renewable-Energy-Driven Future, 125-168. [DOI:10.1016/B978-0-12-820539-6.00004-2]
12. Kothari, D. P., Ranjan, R., & Singal, K. C. (2021). Renewable energy sources and emerging technologies.
13. Panwar, N. L., Kaushik, S. C., & Kothari, S. (2011). Role of renewable energy sources in environmental protection: A review. Renewable and sustainable energy reviews, 15(3), 1513-1524. [DOI:10.1016/j.rser.2010.11.037]
14. Hargreaves, T., & Middlemiss, L. (2020). The importance of social relations in shaping energy demand. Nature Energy, 5(3), 195-201. [DOI:10.1038/s41560-020-0553-5]
15. Nazir, M. S., Alturise, F., Alshmrany, S., Nazir, H. M. J., Bilal, M., Abdalla, A. N., ... & M. Ali, Z. (2020). Wind generation forecasting methods and proliferation of artificial neural network: A review of five years research trend. Sustainability, 12(9), 3778. [DOI:10.3390/su12093778]
16. Kadhem, A. A., Wahab, N. A., Aris, I., Jasni, J., Abdalla, A. N., & Matsukawa, Y. (2017). Reliability assessment of generating systems with wind power penetration via BPSO. Journal of International Journal on Advanced Science, Engineering and Information Technology, 7(4), 1248-1254. [DOI:10.18517/ijaseit.7.4.2311]
17. Doenges, K., Egido, I., Sigrist, L., Miguélez, E. L., & Rouco, L. (2019). Improving AGC performance in power systems with regulation response accuracy margins using battery energy storage system (BESS). IEEE Transactions on Power Systems, 35(4), 2816-2825. [DOI:10.1109/TPWRS.2019.2960450]
18. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. pearson.
19. Smith, R. G., & Eckroth, J. (2017). Building AI applications: Yesterday, today, and tomorrow. Ai Magazine, 38(1), 6-22. [DOI:10.1609/aimag.v38i1.2709]
20. Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330. [DOI:10.1016/j.ijme.2019.100330]
21. Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330. [DOI:10.1016/j.ijme.2019.100330]
22. Di Vaio, A., Boccia, F., Landriani, L., & Palladino, R. (2020). Artificial intelligence in the agri-food system: Rethinking sustainable business models in the COVID-19 scenario. Sustainability, 12(12), 4851. [DOI:10.3390/su12124851]
23. Zhang, Y., Anoopkumar, A. N., Aneesh, E. M., Pugazhendhi, A., Binod, P., Kuddus, M., ... & Sindhu, R. (2023). Advancements in the energy-efficient brine mining technologies as a new frontier for renewable energy. Fuel, 335, 127072. [DOI:10.1016/j.fuel.2022.127072]
24. Jha, S. K., Bilalovic, J., Jha, A., Patel, N., & Zhang, H. (2017). Renewable energy: Present research and future scope of Artificial Intelligence. Renewable and Sustainable Energy Reviews, 77, 297-317. [DOI:10.1016/j.rser.2017.04.018]
25. Farghali, M., Osman, A. I., Chen, Z., Abdelhaleem, A., Ihara, I., Mohamed, I. M., ... & Rooney, D. W. (2023). Social, environmental, and economic consequences of integrating renewable energies in the electricity sector: a review. Environmental Chemistry Letters, 21(3), 1381-1418. [DOI:10.1007/s10311-023-01587-1]
26. Gaudio, M. T., Coppola, G., Zangari, L., Curcio, S., Greco, S., & Chakraborty, S. (2021). Artificial intelligence-based optimization of industrial membrane processes. Earth systems and environment, 5(2), 385-398. [DOI:10.1007/s41748-021-00220-x]
27. Borni, A., Abdelkrim, T., Bouarroudj, N., Bouchakour, A., Zaghba, L., Lakhdari, A., & Zarour, L. (2017). Optimized MPPT controllers using GA for grid connected photovoltaic systems, comparative study. Energy Procedia, 119, 278-296. [DOI:10.1016/j.egypro.2017.07.084]
28. Akhter, M. N., Mekhilef, S., Mokhlis, H., & Mohamed Shah, N. (2019). Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renewable Power Generation, 13(7), 1009-1023. [DOI:10.1049/iet-rpg.2018.5649]
29. Su, S., Yan, X., Agbossou, K., Chahine, R., & Zong, Y. (2022, March). Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study. In Journal of Physics: Conference Series (Vol. 2208, No. 1, p. 012013). IOP Publishing. [DOI:10.1088/1742-6596/2208/1/012013]
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