Author : Suvidha Biradar 1
Date of Publication :25th April 2017
Abstract: In the Energy Management system, the main constraints are accurate metering, energy monitoring and implementation of visual data for consumer load profile. This Project is intended in designing a system at home which monitors the energy consumption of each device, along with the smart energy meter, which is designed to calculate the total energy consumption of the home. All the data calculations will be handled by the Arduino energy meter for the accurate readings. A server will be created with appropriate channels to monitor the energy consumption from each of the devices respectively. These data will be uploaded to the server at the monitoring station. Considering all these data individual energy load profile for each of the devices is displayed on the web-page. Accordingly the analysis can be made for the precise usage or energy consumption of each device in order to further reduce the usage of the device which is drawing the maximum amount of energy. These monitoring reports would help consumers to take the required action in order to improvise the energy usage.
Reference :
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[1] Ricardo Augusto Souza Fernandes, Ivan Nunes da Silva,and Mario Oleskovicz, "Load Profile Identification Interface for Consumer Online Monitoring Purposes in Smart Grids," IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 3, AUGUST 2013.
[2] Francesco Benzi, Norma Anglani, Ezio Bassi, and Lucia Frosini, "Electricity Smart Meters Interfacing the Households," IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 58, NO. 10, OCTOBER 2011.
[3] Usman Ali, Concettina Buccella, Carlo Cecati, "Households Electricity Consumption Analysis with Data Mining Techniques." Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy, ©2016 IEEE.
[4] Desh Deepak Sharma, and S.N.Singh, "Electrical Load Profile Analysis and Peak Load Assessment using Clustering Technique," Electrical Engineering Department, Indian Institute of Technology Kanpur, India ©2014 IEEE.
[5] N. Gatsis and G. B. Giannakis, “Residential load control: Distributed Scheduling and convergence with lost AMI messages,” IEEE Trans. Smart Grid, vol. 3, no. 2, pp. 770–786, 2012.
[6] J. D. Hobby, A. Shoshitaishvili, and G. H. Tucci, “Analysis and methodology to segregate residential electricity consumption in different taxonomies,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 217–224, Mar. 2012.
[7] D. Huang, H. Zareipour, Shoshitaishvili, and G. H. Tucci, “Data mining for electricity price classification and the application to demand-side management,” IEEE Trans. Smart Grid, vol. 3, no. 2, pp. 808–817, Feb. 2012.
[8] P. Palensky and D. Dietrich, “Demand side management: Demand response, intelligent energy systems, and smart loads,” IEEE Trans. Ind. Inf., vol. 7, no. 3, pp. 381–388, Aug. 2011.
[9]Tri Kurniawan Wijaya, Tanuja Ganu, Dipanjan Chakraborty, Karl Aberer, and Deva P Seetharam. Consumer segmentation and knowledge extraction from smart meter and survey data. In SDM, pages 226–234. SIAM, 2014.
[10] Dipl-Wi-Ing Christoph Flath, Dipl-Wi-Ing David Nicolay, Tobias Conte, PD Dr Clemens van Dinther, and Lilia Filipova-Neumann. Cluster analysis of smart metering data. Business & Information Systems Engineering, 4(1):31–39, 2012.
[11] Luis Hern´andez, Carlos Baladr´on, Javier M Aguiar, Bel´en Carro, and Antonio S´anchezEsguevillas. Classification and clustering of electricity demand patterns in industrial parks. Energies, 5(12):5215–5228, 2012.
[12] A. Haney, T. Jamasb, and M. Pollitt, “Smart Metering and Electricity Demand: Technology, Economics and International Experience,” Cambridge Working Paper in Economics, 2009, [working paper], [online]. Available at http://www.econ.cam.ac.uk/dae/repec/cam/pdf/ cwpe0905.pdf
[13] Fintan McLoughlin, Aidan Duffy, and Michael Conlon. A clustering approach to domestic electricity load profile characterisation using smart metering data. Applied energy, 141:190–199, 2015.