NDLI: Residential electric load disaggregation for low-frequency utility applications

Content Provider	IEEE Xplore Digital Library
Author	Guanchen Zhang Wang, G. Farhangi, H. Palizban, A.
Copyright Year	2015
Description	Author affiliation: Sch. of Mechatron. Syst. Eng., Simon Fraser Univ., Burnaby, BC, Canada (Guanchen Zhang; Wang, G.) \|\| Group of Adv. Inf. Technol., British Columbia Inst. of Technol., Burnaby, BC, Canada (Farhangi, H.; Palizban, A.)
Abstract	Recent load disaggregation approaches take advantage of artificial intelligent techniques and require low sampling frequency. From utility perspective, intrusive data for training are not available due to privacy and the sampling frequency may be too low to recognize meaningful signatures. This paper proposes a 1-hour frequency disaggregation algorithm for real and reactive energy without knowing what appliances are in a specific house/apartment. The proposed algorithm is particularly developed concerning utility's constraints. A database of typical types of appliances in BC, Canada is built. Based on BC Hydro's smart meter data over a year, the most probable appliances in a specific house/apartment are firstly inferred through likelihood maximization and energy consumption matching. The disaggregation is then implemented by an integer multi-objective Genetic Algorithm tuned by appliance dependence rules. The results show that despite of high uncertainty, more than 50% of energy consumption could be disaggregated for random houses/apartments.
Starting Page	1
Ending Page	5
File Size	491361
Page Count	5
File Format	PDF
ISBN	9781467380409
DOI	10.1109/PESGM.2015.7286502
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Home appliances Optimization Reactive power Energy consumption Space heating Water heating Hidden Markov models Load Forecast Load Disaggregation Smart Meters Data Mining
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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