IOT-SEMS: An IoT-Driven Energy Management Solution for Industry 4.0

Abstract

The rising demand for energy efficiency in industrial environments has led to the emergence of intelligent monitoring and control systems. This research presents the development of an IoT-based Smart Energy Management System (SEMS) tailored for industrial applications. The system integrates a Raspberry Pi, Arduino microcontroller, TCS3200 colour sensor, and a 4-channel relay module to enable real-time monitoring, classification, and control of electrical devices based on energy usage and operational conditions. The Raspberry Pi serves as the central processing unit, while the Arduino interfaces with sensors and actuators to collect data and execute control signals. The TCS3200 colour sensor is employed to detect status indicators such as device operating states or color-coded signals on machinery, enabling context-aware automation. The 4-channel relay provides the switching mechanism to control multiple loads efficiently. Through Wi-Fi connectivity, the system transmits data to the cloud, allowing for remote access, real-time alerts, and performance analytics. Experimental results validate the system\'s ability to optimize energy usage, enhance operational control, and support predictive maintenance in industrial setups. This work contributes a scalable and cost-effective solution for intelligent energy management in Industry 4.0 ecosystems.

Introduction

In Industry 4.0, intelligent and energy-efficient systems are crucial for industrial settings with high power demands and continuous operation. Traditional energy management lacks real-time automation and flexibility, but IoT technologies offer dynamic solutions for smart energy control. This research develops an IoT-based Smart Energy Management System (SEMS) for industry, utilizing a Raspberry Pi as the central processor and an Arduino for sensor interfacing. A TCS3200 color sensor reads color-coded signals from analog meters, and a 4-channel relay module controls power to industrial loads remotely.

The system enables real-time monitoring, automated control, and data transmission via Wi-Fi to a cloud platform (ThingSpeak), supporting energy analytics, remote diagnostics, and predictive maintenance. This setup aims to be cost-effective, scalable, and user-friendly for smart factories.

The literature review highlights the evolution from GSM-based energy meters to cloud-integrated IoT systems, stressing the role of wireless sensor networks, cloud computing, and machine learning in energy management. The proposed layered architecture includes analog meter sensing, optical color detection, Arduino data processing, Raspberry Pi for communication and control, Wi-Fi for cloud connectivity, and web/mobile interfaces for user access.

Results demonstrate the system’s capability to measure energy consumption accurately and manage loads efficiently, converting LED pulses from meters into standardized energy units, facilitating cost and prepaid balance calculations. This research offers a practical framework for upgrading traditional industrial energy meters to smart IoT-enabled solutions.

Conclusion

This research presents the successful development of an IoT-based Smart Energy Management System tailored for industrial applications, leveraging the capabilities of a Raspberry Pi, Arduino board, TCS3200 color sensor, and a 4-channel relay module. The system is designed to monitor energy consumption in real-time, interpret analog meter readings using a color sensor, and automate load control based on intelligent logic and threshold values. By integrating cloud connectivity via Wi-Fi and utilizing platforms such as ThingSpeak for remote monitoring, the proposed model ensures energy usage is both visible and manageable from any location. The use of low-cost, open-source hardware makes the solution affordable and scalable, making it suitable for both small and medium-scale industrial environments. Furthermore, features like prepaid monitoring, automated switching, and SMS alert notifications improve operational efficiency and energy accountability. In conclusion, this system demonstrates a practical, efficient, and intelligent approach to industrial energy management in the era of Industry 4.0, with significant potential for future enhancements through the incorporation of machine learning and predictive analytics.

References

[1] L. C. Saikia, \"GSM enabled smart energy meter and automation of home appliances\", Proceedings of the International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), June 12-13, 2015. [2] Y. Cheng et al., \"Running state evaluation of electric energy meter\", IEEE Workshop on Electronics, Computer and Applications, May 8-9, 2014. [3] O. O. Edward, \"An Energy Meter Reader with Load Control Capacity and Secure Switching Using a Password Based Relay Circuit\", Proceedings of the Annual Global Online Conference on Information and Computer Technology, Dec 3-5, 2014. [4] M. N. Sahana et al., \"Home energy management leveraging open IoT protocol stack\", Proceedings of the IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, India, Dec 10-12, 2015. [5] H. Arasteh et al., \"IOT-based smart cities: A survey\", Proceedings of the IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy, June 7-10, 2016. [6] A. Kumar et al., \"Location-Based Routing Protocols for Wireless Sensor Networks: A Survey\", Wireless Sensor Network, Vol. 9 (1), 2017. [7] C. Kaiwen et al., \"An intelligent home appliance control-based on WSN for smart buildings\", Proceedings of the IEEE International Conference on Sustainable Energy Technologies (ICSET), Hanoi, Vietnam, Nov 14-16, 2016. [8] M. Zhao et al., \"A Cloud-Based Network Architecture for Big Data Services\", Proceedings of 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress, Aug 8-12, 2016. [9] J. Liu et al., \"A survey on IoT technologies and applications\", IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1024-1034, Dec 2016. [10] M. A. Al-Shammari et al., \"Smart home energy management system using IoT technology\", Journal of Engineering Research and Applications, vol. 7, no. 3, pp. 1-6, 2017. [11] A. K. Singh et al., \"IoT based smart energy meter for efficient energy management\", International Journal of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 6, pp. 1-6, 2017. [12] S. S. Rao et al., \"IoT enabled smart energy management system for smart cities\", Journal of Intelligent Information Systems, vol. 49, no. 2, pp. 1-15, 2018. [13] M. R. Bhuiyan et al., \"IoT based smart home energy management system using machine learning algorithm\", Journal of Intelligent & Robotic Systems, vol. 97, no. 1, pp. 1-18, 2020

Copyright

Copyright © 2025 Shounak Bandyopadhyay , Goutam Kumar Das , Subhendu Pakhira, Moupiya Chatterjee, Soumyadeep Mondal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.