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
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
C. W. Gellings and J. H. Chamberlin,
Demand Side Management: Concepts and Methods, 2nd ed. Tulsa, OK: PennWell
Books, 1993.
Reducing electricity consumption in
houses, Ontario Home Builders’ Assoc., May 2006, Energy Conservation Committee
Report and Recommendations.
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.
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.
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.
H. Allcott, Real time pricing and
electricity markets, Working Paper, Harvard Univ., Feb. 2009.
Ipakchi and F. Albuyeh, “Grid of the
future,” IEEE Power Energy Mag., vol. 8, no. 4, pp. 52–62, Mar. 2009.
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.
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.
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.
G.M.Masters, Renewable and Efficient
Electric Power Systems. Hoboken, NJ: Wiley, 2004.