Implementation of an Advanced Internet of Things (IoT) Based Intelligent Energy Management System for Home/Industries

Abstract

Energy crisis is one among the prime challenges being faced by many of the countries within the world today. For an enormous extent of the demand of energy in industrial development has increased tremendously. A lot of techniques are suggested like an Energy monitoring and prediction system which is an efficient technique to watch the devices present inside a house or industries and provide notification about their abnormal behavior. In this paper, we have focused on predicting electric energy use of home appliances in a low energy consumption house. Electric energy demands are changed in weekdays and weekend days due to the staying time of home residents. In this project the implementation of an advanced Internet of Things (IoT) based system for intelligent energy management in Industries and the home usage. The users can view their status threw the IOT based android application and the webserver.

Country : India

1 Danti Srinivasulu

  1. Assistant Professor, Department of Computer Science And Engineering, Malla Reddy College of Engineering for Women, Hyderabad -500100, Telangana, India

IRJIET, Volume 2, Issue 10, December 2018 pp. 36-41

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References

  1. C. W. Gellings and J. H. Chamberlin, Demand Side Management: Concepts and Methods, 2nd ed. Tulsa, OK: PennWell Books, 1993.
  2. Reducing electricity consumption in houses, Ontario Home Builders’ Assoc., May 2006, Energy Conservation Committee Report and Recommendations.
  3. Begoli, E.Horey, J,"Design Principles for Effective Knowledge Discovery from Big Data", Software Architecture(WICSA) and European Conference on Software Architecture (ECSA),Joint Working IEEE/IFIP Confeence on,PP:215-218,2012.
  4. P. Dongbaare, S. O. Osuri and S. P. Daniel Chowdhury, "A smart energy management system for residential use," IEEE PES PowerAfrica, Accra, pp. 612-616, 2017.
  5. Steven D. Percy ; Mohammad Aldeen ; Adam Berry, “Residential Demand Forecasting With Solar-Battery Systems: A Survey-Less Approach”, IEEE Transactions on Sustainable Energy , Volume: 9 , Issue: 4 , Page(s): 1499 – 1507, Oct. 2018.
  6. H. Allcott, Real time pricing and electricity markets, Working Paper, Harvard Univ., Feb. 2009.
  7. Ipakchi and F. Albuyeh, “Grid of the future,” IEEE Power Energy Mag., vol. 8, no. 4, pp. 52–62, Mar. 2009.
  8. M.FahriogluandF.L.Alvardo,“Designing incentive compatible on- tracts for effective demand managements,” IEEE Trans. Power Syst., vol. 15, no. 4, pp. 1255–1260, Nov. 2000.
  9. B.Ramanathan and V. Vittal, “A framework for evaluation of advanced direct load control with minimum disruption,” IEEE Trans. Power Syst., vol. 23, no. 4, pp. 1681–1688, Nov. 2008.
  10. M.A.A.Pedrasa,T.D.Spooner,andI.F.MaxGill,“Scheduling of demand side resources using binary particles swarm optimization,” IEEE Trans. Power Syst., vol. 24, no. 3, pp. 1173–1181, Aug. 2009.
  11. G.M.Masters, Renewable and Efficient Electric Power Systems. Hoboken, NJ: Wiley, 2004.