Smart Home Automation Systems: AI Approaches for Monitoring and Reducing Power Consumption

Year : 2025 | Volume : 15 | Issue : 03 | Page : 10 17
    By

    Deepender,

  • Sanju,

  • Kanwal Preet Kaur,

  1. Assistant Professor, Faculty of Computing, Guru Kashi University, Bathinda, Punjab, India
  2. Assistant Professor, Faculty of Agriculture, Guru Kashi University, Bathinda, Punjab, India
  3. Assistant Professor,, Department of Computer Science, Dayanand College, Hisar, Haryana, India

Abstract

This study comprehends a Bluetooth controlled home automation system that users can control remotely through a specific Android platform. Technology advancements lead to homes that acquire greater intelligence. Remote-controlled switches continue replacing standard wall switches because they operate as part of system-wide central control gear in contemporary homes. Today’s users must operate wall-mounted manual switches in every room of the house. Members of both these populations face an additional obstacle for home automation usage. The modern advancements of remote home automation work effectively through smartphone technology. The smartphone application uses this system to send receiver commands which operate the load connected to the device. The receiver features an Arduino board connected to a HC-05 module. Through this technology, users can use specific features of the GUI application to activate device control from any location. The system provides an economical solution which permits users to manage multiple devices simultaneously. Devices under home control need password protection to authorize users for access

Keywords: Smart automation system, relays, smart home, security, Arduino board

[This article belongs to Journal of Power Electronics and Power Systems ]

How to cite this article:
Deepender, Sanju, Kanwal Preet Kaur. Smart Home Automation Systems: AI Approaches for Monitoring and Reducing Power Consumption. Journal of Power Electronics and Power Systems. 2025; 15(03):10-17.
How to cite this URL:
Deepender, Sanju, Kanwal Preet Kaur. Smart Home Automation Systems: AI Approaches for Monitoring and Reducing Power Consumption. Journal of Power Electronics and Power Systems. 2025; 15(03):10-17. Available from: https://journals.stmjournals.com/jopeps/article=2025/view=227457


References

  1. Asadullah M, Raza A. An overview of home automation systems. In: 2016 2nd International Conference on Robotics and Artificial Intelligence (ICRAI). 2016; 1–5.
  2. Dey S, Roy A, Das S. Home automation using Internet of Thing. In: 2016 IEEE 7th AnnualUbiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). 2016; 1–5.
  3.  Lobaccaro G, Carlucci S, Löfström E. A review of systems and technologies for smart homes andsmart grids. Energies. 2016; 9(5): 348.
  4. Kovatsch M, Weiss M, Guinard D. Embedding internet technology for home automation. In: 2010IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA). 2010 Sep; 1–8.
  5. Mennicken S, Huang EM. Hacking the natural habitat: an in-the-wild study of smart homes, theirdevelopment, and the people who live in them. In: International Conference on Pervasive Computing. 2012; 143–60.
  6. Yuneela K, Sharma A. A review paper on technologies used in home automation system. In: 20226th International Conference on Computing Methodologies and Communication (ICCMC). 2022;1–6.
  7. Suseelan AD, Palaniappan S, Hariharan N. Home automation systems – a study. Int J Comput Appl. 2015 Apr; 114(11): 1–4.
  8. Manchanda P, Chahal P, Chaudhary P, Singh R. A review paper on smart home automation. Int J Sci Res Manag Stud. 2016; 3(7): 279–83.
  9. Majeed R, Abdullah NA, Ashraf I, Zikria YB. An intelligent, secure, and smart home automation system. Sci Program. 2020; 2020: 4579291.
  10. Sriskanthan N, Karande A. Bluetooth based home automation system. Microprocess Microsyst. 2002 Aug; 26(6): 281–9.
  11. Mallikraj SN, Rao NT, Sekhar C. Studies on utilization of low cost GSM-Bluetooth based home automation system. Int J Control Autom. 2017 Dec; 10(12): 67–74.
  12. Shrivastava U, Verma JK. A study on 5G technology and its applications in telecommunications. In: 2021 International Conference on Computational Performance Evaluation (ComPE); 2021 Dec; 365–71.
  13. Sanju K, Singh D. Non-linear growth models for acreage, production and productivity of food grains in Haryana. J Exp Agric Int. 2023; 45(7): 1–8.
  14. Sanju, Kumar V, Deepender. Evaluation of imputation techniques for genotypic data of soybean crop under missing completely at random mechanism. Indian J Agric Res. 2023 Oct; 57(5): 701 705.
  15. Deepender, Walia TS. Investigating the role of semantic analysis in automated answer scoring. In: International Conference on Innovations in Computational Intelligence and Computer Vision. Singapore: Springer Nature Singapore; 2022 Nov; 559–71.
  16. Deepender, Walia TS. Investigating the scope of semantic analysis in natural language processing considering accuracy and performance. In: Recent Advances in Computing Sciences. CRC Press; Boca Raton, Florida. 2023; 323–8.
  17. Deepender, Walia TS. Hybrid approach for automated answer scoring using semantic analysis in long Hindi text. Rev Intell Artif. 2024; 38(1): 221–226.
  18. Sanju, Kumar V, Kumari P. Evaluating the performance of Bayesian approach for imputing missing data under different missingness mechanism. Sankhya B. 2024; 86(2): 713–23.
  19. Kumar V, Kumari P. Analysis of incomplete data under different missingness mechanism using imputation methods for wheat genotypes. Curr Agric Res J. 2023; 11(3): 1050–1056.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 27/05/2025
Accepted 15/06/2025
Published 26/08/2025
Publication Time 91 Days
29

Login


My IP

PlumX Metrics