NDLI: Residential appliance identification and future usage prediction from smart meter

Content Provider	IEEE Xplore Digital Library
Author	Basu, K. Debusschere, V. Bacha, S.
Copyright Year	2013
Description	Author affiliation: Grenoble Electr. Eng. Lab. (G2ELab), St. Martin d'Heres, France (Basu, K.; Debusschere, V.; Bacha, S.)
Abstract	Energy management for residential homes and/or offices requires both identification and prediction of the future usages or service requests of different appliances present in the buildings. The aim of this work is to identify residential appliances from aggregate reading at the smart meter and to predict their states in order to minimize their energy consumption. For this purpose, our work is divided in two distinct modules: Appliance identification and future usage prediction. Both identification and prediction are based on multi-label learners which takes inter-appliance co-relation into account. The first part of the paper concerns the identification of electrical appliance usages from the smart meter monitoring. The main objective is to be able to identify individual loads from the aggregate power consumption in a non-intrusive manner. In this work, high energy consuming appliances are identified at 1-hour sampling rate using novel set of meta-features for this domain. The second part of the paper concerns future usage prediction. A comparison of algorithms for future appliance usage prediction using identification and direct consumption reading is presented. This work is based on a real residential dataset, called IRISE: 100 houses monitored every 10 minutes to one hour during one year (including weather informations).
Sponsorship	IEEE Ind. Electron. Soc.
Starting Page	4994
Ending Page	4999
File Size	588366
Page Count	6
File Format	PDF
ISBN	9781479902248
ISSN	1553572X
DOI	10.1109/IECON.2013.6699944
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-10
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Home appliances Prediction algorithms Monitoring Water heating Accuracy Buildings Energy management Smart Grids Non-intrusive load monitoring Multi-label classifier Appliance usage prediction Energy Management Datamining Smart Homes
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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