IoT-Based Energy Monitoring and Optimization System in Industry
Keywords:
Cloud computing, Distributed sensor networks, Energy monitoring, Energy optimization, Industrial Energy management, Internet of Things (IoT), Smart gridAbstract
The IoT-based Energy Monitoring and Optimization System is a cutting-edge project that aims to design and implement a smart energy management system using IoT technology. This system will enable real-time monitoring and optimization of energy consumption in buildings, homes, and industries. The system will consist of sensors and actuators which collects energy consumption data. This data is analyzed, and measures are taken to reduce energy consumption and increase efficiency. The system will also have a user-friendly interface that will allow users to monitor their energy usage and receive alerts when there is an unusual spike in consumption. The IoT-based Energy Monitoring and Optimization System will not only help to reduce energy consumption but also provide valuable insights into energy usage patterns. The main advantage of this system is that costs are saved by reducing energy consumption. By saving energy, the negative impact on the environment will be reduced. An IoT-based Energy Monitoring and Optimization System offers a powerful way to improve energy efficiency, reduce costs, has a positive impact on the environment and contribute to a more sustainable future.
References
S. D. Robinsha and B. Amutha, “IoT architecture for energy management in smart cities,” International Journal of Services Operations and Informatics, vol. 12, no. 4, pp. 325–343, 2023.
Y. Tong, “Energy consumption optimization of an IoT monitoring center based on a max-min ant colony algorithm,” Wireless Communications and Mobile Computing, vol. 2023, no. 1, Art. no. 8178281, 2023.
B. Chakraborty, “Optimization of energy efficiency in smart city IoT sensor networks,” Smart City Insights, vol. 2, no. 1, pp. 17–26, Mar. 2025.
O. V. Gnana Swathika, G. Kanimozhi, E. Umamaheswari, S. Rujay, and S. Saha, “IoT-Based Energy Management System with Data Logging Capability,” Lecture Notes in Electrical Engineering, vol. 688, pp. 547–555, Sep. 2020, doi: https://doi.org/10.1007/978-981-15-7241-8_41.
I. Rojek, D. Mikołajewski, A. Mroziński, M. Macko, T. Bednarek, and K. Tyburek, “Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study,” Energies, vol. 18, no. 7, p. 1706, Mar. 2025, doi: https://doi.org/10.3390/en18071706.
A. A. Mirani, A. Awasthi, N. O’Mahony, and J. Walsh, “Industrial IoT-based energy monitoring system: Using data processing at edge,” IoT, vol. 5, no. 4, pp. 608–633, Sep. 2024.
S. Bandyopadhyay, G. K. Das, S. Pakhira, M. Chatterjee, and S. Mondal, “IOT-SEMS: An IoT-driven energy management solution for Industry 4.0,” IJRASET Journal for Research in Applied Science and Engineering Technology, vol. 13, no. 6, pp. 3464–3468, Jun. 2025.
S. Subramanian, “IoT-enhanced energy management strategies for sustainable smart manufacturing practices,” Internet of Things and Edge Computing Journal, vol. 4, no. 1, pp. 115–152, Jan. 2024.
A. Embergenov, “Enhancing enterprise energy management with IoT-based monitoring systems,” Eurasian Science Review: An International Peer-Reviewed Multidisciplinary Journal, vol. 1, no. 1, Dec. 2023.
F. Shrouf and G. Miragliotta, “Energy management based on Internet of Things: Practices and framework for adoption in production management,” Journal of Cleaner Production, vol. 100, pp. 235–246, Aug. 2015.
C. Yang, S. Lan, L. Wang, W. Shen, and G. G. Huang, “Big data driven edge-cloud collaboration architecture for cloud manufacturing: A software defined perspective,” IEEE Access, vol. 8, pp. 45938–45950, Mar. 2020.
A. A. Majhi and S. Mohanty, “A comprehensive review on internet of things applications in power systems,” IEEE Internet of Things Journal, vol. 11, no. 21, pp. 34896–34923, Aug. 2024.
S. Hanifi, B. Alkali, G. Lindsay, and D. McGlinchey, “Optimizing energy and air consumption in smart manufacturing: An industrial internet of things-based monitoring and efficiency enhancement solution,” Applied Sciences, vol. 15, no. 6, Art. no. 3222, Mar. 2025.