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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.RACCES014
Volume:
10 |
4 |
April 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Electrical and Electronics Engineering
Bharathiyar Institute of Engineering for Women(Autonomous),Salem, Tamilnadu, India
Department of Electrical and Electronics Engineering
Bharathiyar Institute of Engineering for Women(Autonomous),Salem, Tamilnadu, India
Department of Electrical and Electronics Engineering
Bharathiyar Institute of Engineering for Women(Autonomous),Salem, Tamilnadu, India
Department of Electrical and Electronics Engineering
Bharathiyar Institute of Engineering for Women(Autonomous),Salem, Tamilnadu, India
Department of Electrical and Electronics Engineering
Bharathiyar Institute of Engineering for Women(Autonomous),Salem, Tamilnadu, India
Energy consumption is increasing rapidly due to the growth of industrialization, smart buildings, and digital technologies. Traditional energy monitoring systems lack real-time analysis and intelligent decision-making capabilities. This paper proposes an intelligent energy monitoring and optimization system using Artificial Intelligence (AI) and the Internet of Things (IoT). IoT sensors are used to collect real-time data such as voltage, current, temperature, and power consumption from electrical devices. The collected data is transmitted to a cloud platform where AI algorithms analyze energy usage patterns and provide optimization strategies. The system helps in reducing energy wastage, improving efficiency, and enabling predictive energy management. Experimental results show that the proposed system can significantly reduce energy consumption and operational cost in smart environments.
KEYWORDS
The implemented system successfully monitors energy consumption in real time. The collected data helps identify energy usage patterns and inefficient appliances. The AI-based analysis provides recommendations to reduce unnecessary power consumption. Experimental results indicate that energy usage can be reduced by approximately 15–25% through intelligent optimization strategies. The system also provides alerts when abnormal energy consumption is detected.
Sanjay Madakam, R. Ramaswamy, and S. Tripathi, “Internet of Things (IoT): A Literature Review,” International Journal of Computer and Communication Engineering, vol. 4, no. 3, pp. 164– 173, 2015. Giancarlo Fortino and P. Trunfio, Internet of Things Based on Smart Objects: Technology, Middleware and Applications, Springer, 2014. Abdulrahman Alamri et al., “A Survey on SensorCloud: Architecture, Applications, and Approaches,” International Journal of Distributed Sensor Networks, vol. 9, no. 2, 2013. Jayavardhana Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013. Rajkumar Buyya, A. Beloglazov, and J. Abawajy, “Energy-Efficient Management of Data Center Resources for Cloud Computing,” Future Generation Computer Systems, vol. 28, no. 5, pp. 755–768, 2012. Liu Chen, “Intelligent Energy Management System Using IoT and Machine Learning,” IEEE Access, vol. 7, pp. 134562–134573, 2019. Wei Wang et al., “Smart Energy Monitoring System Using IoT Technology,” IEEE Internet of Things Journal, vol. 5, no. 3, pp. 2154–2163, 2018. Mohsen Guizani et al., “Machine Learning for Smart Energy Management in Smart Grids,” IEEE Communications Magazine, vol. 55, no. 1, pp. 122– 128, 2017. Klaus Schwab, The Fourth Industrial Revolution, Crown Business, 2016. Andrea Zanella et al., “Internet of Things for Smart Cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, 2014. Min Chen, S. Mao, and Y. Liu, “Big Data: A Survey,” Mobile Networks and Applications, vol. 19, no. 2, pp. 171–209, 2014.
Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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