ARCHIVES
Year 2026 · Volume 6 · Issue 5
Review Article
Metaheuristic and Nonlinear Optimization for Intelligent Energy and Environmental Systems: A Review of Renewable Generation, Smart Metering, Networks and Environmental Monitoring
R. Kennedy1
M. Santhalakshmi2
S. Balamurali3
1 Assistant Professor, Department of Physics, The American College, Madurai, Tamilnadu, India. 2 Assistant Professor, Department of Computer science and IT,JAIN (Deemed to be) University, Bangalore, Karnataka, India. 3 Associate Professor, Department of Physics (SH), Hindustan College of Engineering and Technology, Coimbatore, Tamilnadu, India.
Published Online: September-October 2026
Pages: 472-477
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260605052
References
1. D. Rolnick et al., “Tackling climate change with machine learning,” ACM Comput. Surv., vol. 55, no. 2, Art. no. 42, 2022, doi: 10.1145/3485128.
2. M. H. Nadimi-Shahraki, S. Taghian, and S. Mirjalili, “An improved grey wolf optimizer for solving engineering problems,” Expert Syst. Appl., vol. 166, p. 113917, 2021, doi: 10.1016/j.eswa.2020.113917.
3. K. Vijetha, S. Pandikumar, T. Margaret Mary, R. R. Tandu, M. Balamurugan, A. Bostani, R. Bhakhar, and P. D. Kumar, “An intelligent HG-GWO framework for performance optimization of grid-connected solar–wind hybrid energy generation,” J. Intell. Decis. Making Inf. Sci., vol. 3, no. 7S, pp. 1343–1358, 2026, doi: 10.59543/jidmis.v3.1826.
4. T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, and H. Zareipour, “Energy forecasting: A review and outlook,” IEEE Open Access J. Power Energy, vol. 7, pp. 376–388, 2020, doi: 10.1109/OAJPE.2020.3029979.
5. C. Jamunadevi, N. Rajasekaran, S. Pandikumar, R. Raja, and K. P. Narendra Prasanth, “Energy performance forecasting in buildings using machine learning,” in Proc. Int. Conf. Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 2024, pp. 1–6, doi: 10.1109/IC3IoT60841.2024.10550233.
6. Y. Wang, Q. Chen, T. Hong, and C. Kang, “Review of smart meter data analytics: Applications, methodologies, and challenges,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3125–3148, 2019, doi: 10.1109/TSG.2018.2818167.
7. S. Pandikumar, A. Bostani, A. Srinivasan, G. Menaka, J. Chandhini, R. Sabitha, and K. Sathishkumar, “Fuzzy logic-driven nonlinear optimization for resource allocation in 6G networks,” Results Nonlinear Anal., vol. 8, no. 2, pp. 239–252, 2025, doi: 10.31838/rna/2025.08.02.019.
8. S. Pandikumar, S. P. Kabilan, and L. Amalraj, “Green IT: A study and analysis of environmental impact of social networks and search engines,” Int. J. Comput. Appl., vol. 60, no. 6, pp. 17–22, Dec. 2012, doi: 10.5120/9695-4135.
9. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
10. T. Margaret Mary, G. Ramanathan, K. Soumya, and G. Clinton, “A technique for effective metering of power consumption in smart grid environment,” in Proc. 2nd Int. Conf. Smart Electronics and Communication (ICOSEC), 2021, pp. 702–709, doi:
10.1109/ICOSEC51865.2021.9591902.
11. M. Bourdeau, X. Q. Zhai, E. Nefzaoui, X. Guo, and P. Chatellier, “Modeling and forecasting building energy consumption: A review of data-driven techniques,” Sustain. Cities Soc., vol. 48, p. 101533, 2019, doi: 10.1016/j.scs.2019.101533.
12. K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang, “The roadmap to 6G: AI empowered wireless networks,” IEEE Commun. Mag., vol. 57, no. 8, pp. 84–90, 2019, doi: 10.1109/MCOM.2019.1900271.
13. S. Pandikumar, A. Bostani, D. Sundaranarayana, I. Fakhriddin, D. Alimjon, U. Ilkhom, and A. Juraboyev, “AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems,” Results Nonlinear Anal., vol. 8, no. 3, pp. 36–46, 2025, doi: 10.31838/rna/2025.08.03.005.
14. S. Pandikumar and S. P. Kabilan, “Principles and holistic design of green web portal,” Int. J. Comput. Appl., vol. 65, no. 9, pp. 23–29, Mar. 2013, doi: 10.5120/10953-5913.
15. S. Vetrivel, S. Pandikumar, and M. Athigopal, “Analysis and principles of green UI design for web portals,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 2, no. 1, 2014.
16. S. Pandikumar and M. Sumathi, “Investigation on the revolution and need of energy efficient smartphone development,” Int. J. Comput. Appl., vol. 117, no. 11, pp. 1–5, May 2015, doi: 10.5120/20595-3091.
17. S. Pandikumar and M. Sumathi, “Energy efficient algorithm for high speed packet data transfer on smartphone environment,” Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 2644–2658, Aug. 2019, doi: 10.35940/ijeat.F8668.088619.
18. S. Pandikumar, S. Bharani Sethupandian, B. Arivazhagan, P. Rajeswari, “Towards smartphone energy optimization through users operating pattern discovery,” in Sentiment Analysis and Deep Learning (Adv. Intell. Syst. Comput., vol. 1432), S. Shakya, K.-L. Du, and K. Ntalianis, Eds. Singapore: Springer, 2023, doi: 10.1007/978-981-19-5443-6_47.
19. S. L. Ullo and G. R. Sinha, “Advances in smart environment monitoring systems using IoT and sensors,” Sensors, vol. 20, no. 11, p. 3113, 2020, doi: 10.3390/s20113113.
20. S. Pandikumar, B. Sapaev, K. Jafarov, J. Saidmuradova, G. Bekhruz, S. Elmurodov, and S. Muratova, “An artificial intelligence and genetic algorithm optimized biosensor system for real-time detection of environmental toxins and their biomedical impact,” Nat. Eng. Sci., vol. 10, no. 3, pp. 561–574, 2025, doi: 10.28978/nesciences.1811144.
21. N. Meenakshi, V. Pandimurugan, and S. Rajasoundaran, “IoT-based water management system for a healthy life,” in The Industrial Internet of Things (IIoT). Hoboken, NJ, USA: Wiley, 2022, pp. 229–248, doi: 10.1002/9781119769026.ch9.
22. O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. Wang, “Internet of Things for the future of smart agriculture: A comprehensive survey of emerging technologies,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 718–752, 2021, doi: 10.1109/JAS.2021.1003925.
23. S. Pandikumar, S. P. Kabilan, and S. Ambethkar, “Architecture of GSM based WSN for greenhouse monitoring system in ambient intelligence environment,” Int. J. Comput. Appl., vol. 63, no. 6, pp. 1–5, Feb. 2013, doi: 10.5120/10467-5187.
24. V. Pandimurugan, R. Mandviya, A. Gadgil, K. Prakhar, and A. Datar, “IoT based smart beekeeping monitoring system,” in Proc. 4th Int. Conf. Computing and Communications Technologies (ICCCT), 2021, pp. 65–70, doi: 10.1109/ICCCT53315.2021.9711901.
25. T. Megala, K. Srinivasan, K. Sathishkumar, M. Ramalingam, S. Pandikumar, A. Mummoorthy, and R. Naveenkumar, “A systematic investigation of climate change impacts and disaster risk reduction strategies using data-driven analytical models,” Int. J. Artif. Intell. Mach. Learn., vol. 6, no. 5s, pp. 252–262, 2026.
26. M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” J. Comput. Phys., vol. 378, pp. 686–707, 2019, doi: 10.1016/j.jcp.2018.10.045.
27. B. S. Sethupandian, T. Margaret Mary, K. S. Divya, B. Sivaranjani, R. Naveenkumar, V. Yamuna, and A. Selvaraj, “Quantum calculus-based nonlinear analysis and neural approximation of fuzzy partial differential models in fluid dynamics,” Results Nonlinear Anal., vol. 9, no. 1, pp. 17–28, 2026, doi: 10.31838/rna/2026.09.01.003.
2. M. H. Nadimi-Shahraki, S. Taghian, and S. Mirjalili, “An improved grey wolf optimizer for solving engineering problems,” Expert Syst. Appl., vol. 166, p. 113917, 2021, doi: 10.1016/j.eswa.2020.113917.
3. K. Vijetha, S. Pandikumar, T. Margaret Mary, R. R. Tandu, M. Balamurugan, A. Bostani, R. Bhakhar, and P. D. Kumar, “An intelligent HG-GWO framework for performance optimization of grid-connected solar–wind hybrid energy generation,” J. Intell. Decis. Making Inf. Sci., vol. 3, no. 7S, pp. 1343–1358, 2026, doi: 10.59543/jidmis.v3.1826.
4. T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, and H. Zareipour, “Energy forecasting: A review and outlook,” IEEE Open Access J. Power Energy, vol. 7, pp. 376–388, 2020, doi: 10.1109/OAJPE.2020.3029979.
5. C. Jamunadevi, N. Rajasekaran, S. Pandikumar, R. Raja, and K. P. Narendra Prasanth, “Energy performance forecasting in buildings using machine learning,” in Proc. Int. Conf. Communication, Computing and Internet of Things (IC3IoT), Chennai, India, 2024, pp. 1–6, doi: 10.1109/IC3IoT60841.2024.10550233.
6. Y. Wang, Q. Chen, T. Hong, and C. Kang, “Review of smart meter data analytics: Applications, methodologies, and challenges,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3125–3148, 2019, doi: 10.1109/TSG.2018.2818167.
7. S. Pandikumar, A. Bostani, A. Srinivasan, G. Menaka, J. Chandhini, R. Sabitha, and K. Sathishkumar, “Fuzzy logic-driven nonlinear optimization for resource allocation in 6G networks,” Results Nonlinear Anal., vol. 8, no. 2, pp. 239–252, 2025, doi: 10.31838/rna/2025.08.02.019.
8. S. Pandikumar, S. P. Kabilan, and L. Amalraj, “Green IT: A study and analysis of environmental impact of social networks and search engines,” Int. J. Comput. Appl., vol. 60, no. 6, pp. 17–22, Dec. 2012, doi: 10.5120/9695-4135.
9. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
10. T. Margaret Mary, G. Ramanathan, K. Soumya, and G. Clinton, “A technique for effective metering of power consumption in smart grid environment,” in Proc. 2nd Int. Conf. Smart Electronics and Communication (ICOSEC), 2021, pp. 702–709, doi:
10.1109/ICOSEC51865.2021.9591902.
11. M. Bourdeau, X. Q. Zhai, E. Nefzaoui, X. Guo, and P. Chatellier, “Modeling and forecasting building energy consumption: A review of data-driven techniques,” Sustain. Cities Soc., vol. 48, p. 101533, 2019, doi: 10.1016/j.scs.2019.101533.
12. K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang, “The roadmap to 6G: AI empowered wireless networks,” IEEE Commun. Mag., vol. 57, no. 8, pp. 84–90, 2019, doi: 10.1109/MCOM.2019.1900271.
13. S. Pandikumar, A. Bostani, D. Sundaranarayana, I. Fakhriddin, D. Alimjon, U. Ilkhom, and A. Juraboyev, “AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems,” Results Nonlinear Anal., vol. 8, no. 3, pp. 36–46, 2025, doi: 10.31838/rna/2025.08.03.005.
14. S. Pandikumar and S. P. Kabilan, “Principles and holistic design of green web portal,” Int. J. Comput. Appl., vol. 65, no. 9, pp. 23–29, Mar. 2013, doi: 10.5120/10953-5913.
15. S. Vetrivel, S. Pandikumar, and M. Athigopal, “Analysis and principles of green UI design for web portals,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 2, no. 1, 2014.
16. S. Pandikumar and M. Sumathi, “Investigation on the revolution and need of energy efficient smartphone development,” Int. J. Comput. Appl., vol. 117, no. 11, pp. 1–5, May 2015, doi: 10.5120/20595-3091.
17. S. Pandikumar and M. Sumathi, “Energy efficient algorithm for high speed packet data transfer on smartphone environment,” Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 2644–2658, Aug. 2019, doi: 10.35940/ijeat.F8668.088619.
18. S. Pandikumar, S. Bharani Sethupandian, B. Arivazhagan, P. Rajeswari, “Towards smartphone energy optimization through users operating pattern discovery,” in Sentiment Analysis and Deep Learning (Adv. Intell. Syst. Comput., vol. 1432), S. Shakya, K.-L. Du, and K. Ntalianis, Eds. Singapore: Springer, 2023, doi: 10.1007/978-981-19-5443-6_47.
19. S. L. Ullo and G. R. Sinha, “Advances in smart environment monitoring systems using IoT and sensors,” Sensors, vol. 20, no. 11, p. 3113, 2020, doi: 10.3390/s20113113.
20. S. Pandikumar, B. Sapaev, K. Jafarov, J. Saidmuradova, G. Bekhruz, S. Elmurodov, and S. Muratova, “An artificial intelligence and genetic algorithm optimized biosensor system for real-time detection of environmental toxins and their biomedical impact,” Nat. Eng. Sci., vol. 10, no. 3, pp. 561–574, 2025, doi: 10.28978/nesciences.1811144.
21. N. Meenakshi, V. Pandimurugan, and S. Rajasoundaran, “IoT-based water management system for a healthy life,” in The Industrial Internet of Things (IIoT). Hoboken, NJ, USA: Wiley, 2022, pp. 229–248, doi: 10.1002/9781119769026.ch9.
22. O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. Wang, “Internet of Things for the future of smart agriculture: A comprehensive survey of emerging technologies,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 718–752, 2021, doi: 10.1109/JAS.2021.1003925.
23. S. Pandikumar, S. P. Kabilan, and S. Ambethkar, “Architecture of GSM based WSN for greenhouse monitoring system in ambient intelligence environment,” Int. J. Comput. Appl., vol. 63, no. 6, pp. 1–5, Feb. 2013, doi: 10.5120/10467-5187.
24. V. Pandimurugan, R. Mandviya, A. Gadgil, K. Prakhar, and A. Datar, “IoT based smart beekeeping monitoring system,” in Proc. 4th Int. Conf. Computing and Communications Technologies (ICCCT), 2021, pp. 65–70, doi: 10.1109/ICCCT53315.2021.9711901.
25. T. Megala, K. Srinivasan, K. Sathishkumar, M. Ramalingam, S. Pandikumar, A. Mummoorthy, and R. Naveenkumar, “A systematic investigation of climate change impacts and disaster risk reduction strategies using data-driven analytical models,” Int. J. Artif. Intell. Mach. Learn., vol. 6, no. 5s, pp. 252–262, 2026.
26. M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” J. Comput. Phys., vol. 378, pp. 686–707, 2019, doi: 10.1016/j.jcp.2018.10.045.
27. B. S. Sethupandian, T. Margaret Mary, K. S. Divya, B. Sivaranjani, R. Naveenkumar, V. Yamuna, and A. Selvaraj, “Quantum calculus-based nonlinear analysis and neural approximation of fuzzy partial differential models in fluid dynamics,” Results Nonlinear Anal., vol. 9, no. 1, pp. 17–28, 2026, doi: 10.31838/rna/2026.09.01.003.
Related Articles
2026
A Strategic Framework for Depth-Dependent Hydroelectric Conversion along the Indian Coastline
2026
Reimagining Development in India: A Critical Analysis of the Viksit Bharat Vision
2026
AI-Enabled Image Description: Bridging the Gap for the Visually Impaired
2026
Perceived Occupational Risks of Emergency Medical Services Personnel
2026
Origin, Growth and recent Development of Integrated Reporting (IR): A theoretical Review
2026
Smart Hostel Management System
Share Article
Or copy link
https://www.ijrtmr.com/archives/metaheuristic-and-nonlinear-optimization-for-intelligent-energy-and-environmental-systems-a-review-of-renewable-generation-smart-metering-networks-and-environmental-monitoring
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.