Detecting and Reducing Phantom Power: A Human-Centric IoT Approach

Prof. Sejal Waghe; Unnati Kuradkar; Simran Prajapati; Roshani Mohane

doi:10.26706/ijceae.7.4.20260710

Abstract

A significant amount of electricity is unintentionally wasted in daily life [1]. This occurs because electronic devices continue to draw power even when they are switched off or not actively in use [1]. Although technologies like smart meters and energy monitoring systems are available, user awareness regarding this hidden energy consumption remains low [2]. Because of this, electricity bills increase and there is also a negative impact on the environment [1]. This paper proposes a system called Phantom Power Detective. This system is based on Internet of Things (IoT) technology and is used to find standby power usage of individual electrical appliances [2]. The objective is to present this information in an intuitive and user-friendly manner so that users can understand where electricity is being wasted. When users become aware of such hidden consumption, they are more likely to reduce it and save energy [1]. Most existing energy monitoring systems only show total electricity consumption [2]. They do not explain which device is wasting power or why it is happening. The proposed system operates differently. It measures power usage of each appliance in real time and then studies the data to find hidden usage patterns [2]. These patterns are explained in simple terms so that general users can easily interpret them. The focus of this system is on user understanding, daily habits and energy saving. It considers phantom power as a problem related to human behaviour and awareness, not only as an electrical issue [1]. From experimental use cases, it is seen that the system can detect phantom power usage of individual appliances and can also give an idea of how much energy can be saved. Overall, this work shows a new and simple way to manage energy by focusing on user behaviour and awareness.

Keywords

Phantom Power Internet of Things Energy Analytics Smart Plugs Behavioral Data Mining Sustainable Computing

References

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[ref1] 1. International Energy Agency , “Energy Efficiency 2022,” IEA Publications, Tech. Rep., 2022.

[ref2] 2. U.S. Department of Energy , “Standby power consumption,” 2015.

[ref3] 3. A. Meier , “Standby power use in homes,” Energy and Buildings, vol. 45, pp. 92–99, 2012.

[ref4] 4. International Electrotechnical Commission , “Household electrical appliances – Measurement of standby power,” IEC Standard 62301, 2011.

[ref5] 5. S. Darby , “The effectiveness of feedback on energy consumption,” Environmental Change Institute, University of Oxford, Tech. Rep., 2006.

[ref6] 6. G. W. Hart , “Nonintrusive appliance load monitoring,” Proc. IEEE, vol. 80, no. 12, pp. 1870–1891, 1992.

[ref7] 7. H. Kim et al. , “Unsupervised disaggregation of low frequency power measurements,” in Proc. SIAM Int. Conf. Data Mining, 2011.

[ref8] 8. M. Froehlich et al. , “The design of eco-feedback systems,” in Proc. SIGCHI Conf. Human Factors Comput. Syst., 2009.

[ref9] 9. A. Molina-Markham et al. , “Energy dashboard design,” in Proc. SIGCHI Conf. Human Factors Comput. Syst., 2010.

[ref10] 10. J. Kelly and W. Knottenbelt , “Neural NILM: Deep neural networks applied to energy disaggregation,” in Proc. ACM BuildSys, 2015.

[ref11] 11. S. Gupta , “IoT-based smart energy systems,” IEEE, 2021.

[ref12] 12. T. Nguyen , “Real-time energy monitoring using IoT,” IEEE Access, 2020.

[ref13] 13. H. Lee , “Smart home energy management systems,” IEEE, 2022.

[ref14] 14. D. Singh , “Smart grid technology and applications,” IEEE, 2023.

[ref15] 15. B. Zhao , “Energy analytics and IoT,” Elsevier, 2023.

[ref16] 16. A. Mishra , “Smart plug based monitoring systems,” Springer, 2022.

[ref17] 17. K. Patel , “IoT and sustainable computing,” Springer, 2021.

[ref18] 18. R. Brown , “Standby power reduction techniques,” Lawrence Berkeley National Laboratory (LBNL), Tech. Rep., 2019.

[ref19] 19. M. Zeifman , “Nonintrusive load monitoring review,” IEEE, 2011.

[ref20] 20. L. Bergman , Energy Efficiency in Smart Homes. Hoboken, NJ, USA: Wiley, 2020.

[ref21] 21. P. Siano , “Demand response and smart grids,” Renewable Energy Journal, 2014.

[ref22] 22. F. Mattern , “From smart devices to smart everyday objects,” Springer, 2010.

[ref23] 23. D. Evans , “The Internet of Things: How the next evolution of the Internet is changing everything,” Cisco Internet Business Solutions Group (IBSG), Tech. Rep., 2011.

[ref24] 24. R. Buyya , Cloud Computing: Principles and Paradigms. Hoboken, NJ, USA: Wiley, 2011.

[ref25] 25. A. Dunkels , “IoT protocols and applications,” IEEE, 2012.

[ref26] 26. C. Perera , “Context-aware computing for IoT,” IEEE, 2014.

[ref27] 27. J. Gubbi et al. , “Internet of Things (IoT) vision: Architectural elements,” Future Generation Computer Systems, 2013.

[ref28] 28. A. Zanella et al. , “Internet of Things for smart cities,” IEEE Internet of Things Journal, 2014.

[ref29] 29. M. Khan , “Energy management in IoT-based systems,” Elsevier, 2022.

[ref30] 30. S. Li , Smart Grid and Renewable Energy Systems. Boca Raton, FL, USA: CRC Press, 2018.

[ref31] 31. A. Sharma and P. Verma , “Smart energy monitoring using IoT-based systems,” International Journal of Multidisciplinary Advances, vol. 3, no. 2, pp. 45–52, 2023.

[ref32] 32. R. Patel and S. Mehta , “Analysis of standby power consumption in household devices,” International Journal of Multidisciplinary Advances, vol. 4, no. 1, pp. 12–19, 2024.

[ref33] 33. K. Singh and M. Gupta , “IoT-based smart plug for energy efficiency,” International Journal of Multidisciplinary Advances, vol. 3, no. 3, pp. 67–74, 2023.

[ref34] 34. N. Joshi and A. Kulkarni , “Behavioral analysis of energy consumption using IoT,” International Journal of Multidisciplinary Advances, vol. 4, no. 2, pp. 25–31, 2024.

[ref35] 35. S. Deshmukh and R. Patil , “Reducing phantom power using smart home technologies,” International Journal of Multidisciplinary Advances, vol. 2, no. 4, pp. 80–86, 2022.