Using IoT-Implement Intensive Care for Air Conditioners with Machine Learning

Authors

  • Nripendra Narayan Das Professor, Department of Information Technology, Manipal University Jaipur, Rajasthan, India
  • K. Somasundaram Professor, Institute Of Information Technology, Saveetha School Of Engineering, Simats, Chennai, Tamil Nadu, India https://orcid.org/0000-0002-0429-7759
  • S. Hemamalini Associate Professor/ CSE, Panimalar Institute of Technology, Chennai, Tamil Nadu, 600123, India
  • K. Valarmathia CSE, Panimalar Engineering College, Chennai, Tamil Nadu, India
  • G. Nagappan Professor & Head, Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, 600127, India
  • S. Hemalatha Professor, Department of Computer Science and Engineering, Panimalar Institute of Technology, Chennai, Tamil Nadu, 600123, India
  • Kamal Gulati Associate Professor, Amity University, Noida, Uttar Pradesh, India https://orcid.org/0000-0002-1186-1426

Keywords:

Internet of Things, machine learning, home automation

Abstract

This study suggests a survey on air conditioner intensive care to check defects As the globe becomes increasingly technologically advanced, various electronic application gadgets are invented and used by individuals. New technological devices are infiltrating our personal life at an increasing rateIt results in the development of an increasing number of electronic gadgets. This necessitates gadget maintenance; otherwise, the devices may be damaged. These electrical gadgets can be serviced by a device expert. The average individual is incapable of anticipating and resuming the functioning of electrical equipment. However, the full scenario, as well as the device's specialist, cannot be attempted in a timely manner. These factors need the purchase of a new equipment. This suggested project will provide a method for preventing electrical equipment failure and replacement. This project uses the Machine language approach to analyses the device's condition and monitoring on a daily basis, comparing it to predetermined machine parameter values. If a difference is discovered between the estimated and predefined state, the machine must be fixed before it fails. The suggested technology calculates the variance and alerts the invented firm and the end user, allowing them to take action before the equipment malfunctions. The Internet of Things (IoT) was used in conjunction with a machine learning algorithm to communicate between gadgets and the enterprise or user. The benefit of this job is that it prevents machine failure, extends the machine's life, and avoids expert repairs that are incorrect. Because it takes a long time to monitor and find defects, the idea of machine learning is employed, which entails the study of machines in order to detect errors quickly. This project shows a simple home-controlled air conditioner system with an IoT device that enables for periodic defect monitoring.

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References

A. Rabl and A. Rialhe, ``Energy signature models for commercial buildings: Test with measured data and interpretation, ''Energy Buildings, vol.19,no.2,pp.143_154,Aug.1992.

Y.Leiand H. Liu, ``Feature selection for high-dimensional data: A fast Correlation-based_ lter solution, ''inProc.20thInt.Conf.Mach. Learn.(ICML), Washington,DC, USA, 2003, pp.856_863.

K.Wangetal.,``AsurveyonenergyInternet:Architecture,approach,andemerging technologies, ''IEEESyst .J.,to be published , doi:10.1109/ JSYST.2016.2639820.

H. Jiang,K.Wang,Y.Wang, M.Gao,andY.Zhang, ``Energybigdata:Asurvey,''IEEE Access,vol. 4,pp. 3844_ 3861,Aug.2016.

E. A. Arens, F. S. Bauman, L. P. Johnston, and H. Zhang, ``Testing oflocalized ventilation systems in a new control led environment chamber, ''Indoor Air, vol.1,no.3,pp.263_281,Sep.1991.

B.Yang, C.Sekhar and A. K. Melikov, `Ceiling mounted personalized ventilation system in hot and humid climate an energy analysis, ''Energy Buildings, vol.42,no.12,pp.2304_2308,Dec.2010.

L. J. Lo and A. Novoselac, ``Localized air-conditioning with occupancy control in an open office,'' EnergyBuildings,vol.42,no.7, pp. 1120_1128, Jul.2010.

R.KohaviandG. H.John,``Wrappersforfeaturesubset selection,''Artif.Intell,vol. 97,nos.1_2, pp.273_324,1997.

Architecture diagram

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Published

10.02.2023

How to Cite

Narayan Das, N. ., Somasundaram, K. ., Hemamalini, S. ., Valarmathia, K. ., Nagappan, G. ., Hemalatha, S. ., & Gulati, K. . (2023). Using IoT-Implement Intensive Care for Air Conditioners with Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 194–203. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/2561

Issue

Vol. 11 No. 3s (2023)

Section

Research Article

License

Copyright (c) 2023 Nripendra Narayan Das, K. Somasundaram, S. Hemamalini, K. Valarmathia, G. Nagappan, S. Hemalatha, Kamal Gulati

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