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All issues Volume 591 (2024) E3S Web Conf., 591 (2024) 01006 References
Open Access
Issue
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
Article Number 01006
Number of page(s) 9
Section Battery Management System and Power Quality
DOI https://doi.org/10.1051/e3sconf/202459101006
Published online 14 November 2024
  1. Liu, Y., Wang, H., & Zhang, S. (2020). “Integration of renewable energy sources in data centers: A review.” IEEE Access, vol. 8, pp. 103473-103488. DOI: 10.1109/ACCESS.2020.2998845 [Google Scholar]
  2. Zheng, T., Huang, J., & Yang, Q. (2021). “Predictive energy management for renewable-powered data centers.” IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1197-1208. DOI: 10.1109/TSG.2020.3032434 [Google Scholar]
  3. Sharma, R., & Kumar, A. (2021). “Lithium-ion battery storage systems for sustainable data centers.” Journal of Renewable and Sustainable Energy, vol. 13, no. 1, pp. 131-142. DOI: 10.1063/5.0053726 [Google Scholar]
  4. Kim, J., Park, H., & Lee, K. (2020). “Hybrid energy storage systems for improving power reliability in renewable energy-based data centers.” Renewable Energy, vol. 162, pp. 870-880. DOI: 10.1016/j.renene.2020.09.032 [Google Scholar]
  5. Chen, X., Fang, Z., & Lin, M. (2021). “AI-based energy management for data centers: A machine learning approach.” Energy Efficiency, vol. 14, no. 5, pp. 1129-1145. DOI: 10.1007/s12053-021-09982-4 [CrossRef] [Google Scholar]
  6. Fang, Y., Li, J., & Yu, P. (2022). “Deep learning-enabled EMS for optimizing energy flow in data centers.” Applied Energy, vol. 310, pp. 118-126. DOI: 10.1016/j.apenergy.2022.118126 [Google Scholar]
  7. Wang, L., Zhou, G., & Zhang, Y. (2019). “Demand response for data centers: A review of smart grid interactions.” IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 2678-2689. DOI: 10.1109/TII.2018.2894289 [Google Scholar]
  8. Zhang, H., Liu, Z., & Wu, F. (2022). “Optimizing grid dependency in data centers using EMS.” IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 315-328. DOI: 10.1109/TSG.2021.3124147 [Google Scholar]
  9. Lin, X., & Lee, J. (2020). “IoT-based EMS for smart grid integration in data centers.” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4521-4534. DOI: 10.1109/JIOT.2020.2979885 [Google Scholar]
  10. Gupta, S., Mehta, K., & Joshi, R. (2021). “Real-time energy management for data centers using IoT.” IEEE Access, vol. 9, pp. 67674-67683. DOI: 10.1109/ACCESS.2021.3082749 [Google Scholar]
  11. Zhou, Z., Sun, J., & Luo, D. (2023). “Sustainable data centers: Reducing carbon footprints with EMS.” Energy Reports, vol. 9, pp. 201-211. DOI: 10.1016/j.egyr.2022.12.004 [Google Scholar]
  12. Jiang, H., Liu, J., & Wang, X. (2022). “Energy-efficient hardware and intelligent cooling for sustainable data centers.” Renewable and Sustainable Energy Reviews, vol. 155, pp. 111-119. DOI: 10.1016/j.rser.2021.111939 [Google Scholar]
  13. Ladu, N.S.D., senthil kumar subburaj., Samikannu, R. “A Review of Renewable Energy Resources. Its Potentials, Benefits, and Challenges in South Sudan, 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021 [Google Scholar]
  14. Pragathi, Bellamkonda, et al. “Evaluation and analysis of soft computing techniques for grid connected photo voltaic system to enhance power quality issues.” Journal of Electrical Engineering & Technology 16 (2021): 1833-1840. [CrossRef] [Google Scholar]
  15. Sathi G.; Deshpande Y.D.; Kumar V.; Garg P.; Singh S.; Pattanaik A.,(2023), “Investigating the Ability of AI Algorithms to Optimize Data Access Processes”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442371. [Google Scholar]
  16. Yuvaraj K.; Yuvaraj S.; Dhabliya D.; Rengarajan A.; Jain N.K.; Agrawal T.,(2023), “Investigating the Potential for Using AI to Improve the Performance of Big Data Access”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442655. [Google Scholar]
  17. Daivagna U.M.; Vinay Kumar S.B.; Agarwal A.; Jha G.; Sharmila P.; Dodia S.J.,(2023), “Exploring the Benefits of Deep Learning for Forecasting Time Series Data”,2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023,Vol.,no.,pp.-.doi:10.1109/SMARTGENCON60755.2023.10442347. [Google Scholar]
  18. Mechanical Property Evaluation of Stir-Squeeze Cast Al-Based Nano SiC Composites, MI Habelalmateen, R Srinivasan, R Pant, RS Kumar, S Sheril, E3S Web of Conferences 491, 02035. [Google Scholar]
  19. Pragathi, B., and P. Ramu. “Authentication Technique for Safeguarding Privacy in Smart Grid Settings.” E3S Web of Conferences. Vol. 540. EDP Sciences, 2024. [Google Scholar]
  20. Pragathi, Bellamkonda, Deepak Kumar Nayak, and Ramesh Chandra Poonia. “Lorentzian adaptive filter for controlling shunt compensator to mitigate power quality problems of solar PV interconnected with grid.” International Journal of Intelligent Information and Database Systems 13.2-4 (2020): 491-506. [CrossRef] [Google Scholar]

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