• Login
    View Item 
    •   Maryland Shared Open Access Repository Home
    • ScholarWorks@UMBC
    • UMBC College of Engineering and Information Technology
    • UMBC Information Systems Department
    • View Item
    •   Maryland Shared Open Access Repository Home
    • ScholarWorks@UMBC
    • UMBC College of Engineering and Information Technology
    • UMBC Information Systems Department
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Iterative Signal Separation Assisted Energy Disaggregation

    Thumbnail
    Files
    IGSC15.pdf (533.2Kb)
    Links to Files
    http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7393701&isnumber=7393673
    Permanent Link
    10.1109/IGCC.2015.7393701
    http://hdl.handle.net/11603/11218
    Collections
    • UMBC Faculty Collection
    • UMBC Information Systems Department
    • UMBC Mathematics and Statistics Department
    • UMBC Student Collection
    Metadata
    Show full item record
    Author/Creator
    Pathak, Nilavra
    Roy, Nirmalya
    Biswas, Animikh
    Date
    2016-01-28
    Type of Work
    8 PAGES
    Text
    conference papers and proceedings preprints
    Citation of Original Publication
    N. Pathak, N. Roy and A. Biswas, "Iterative signal separation assisted energy disaggregation," 2015 Sixth International Green and Sustainable Computing Conference (IGSC), Las Vegas, NV, 2015, pp. 1-8.
    Rights
    This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
    Subjects
    Encoding
    Dictionaries
    Yttrium
    Plugs
    Refrigerators
    Mobile Pervasive & Sensor Computing Lab
    Abstract
    Providing itemized energy consumption in a utility bill is becoming a priority, and perhaps a business practice in the near term. In recent times, a multitude of systems have been developed such as smart plugs, smart circuit breakers etc., for non-intrusive load monitoring (NILM). They are integrated either with the smart meters or at the plug-levels to footprint appliance-level energy consumption patterns in an entire home environment While deploying the existing technologies in a single home is feasible, scaling these technological advancements across thousands of homes in a region is not realized yet. This is primarily due to the cost, deployment complexity, and intrusive nature associated with these types of real deployment. Motivated by these shortcomings, in this paper we investigate the first step to address scalable disaggregation by proposing a disaggregation mechanism that works on a large dataset to accurately deconstruct the cumulative signals. We propose an iterative noise separation based approach to perform energy disaggregation using sparse coding based methodologies which work at the single ingress point of a home, i.e., at the meter level. We performed a ranked iterative signal removal methodology that effectively isolates appliances' individual signal waveform as noise on an aggregate energy datasets with moderate granularity (1 min). We performed experiments on real dataset and obtained approximately 94% energy disaggregation, i.e., disaggregated appliance-wise signal estimation accuracy.


    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.

     

     

    My Account

    LoginRegister

    Browse

    This CollectionBy Issue DateTitlesAuthorsSubjectsType

    Statistics

    View Usage Statistics


    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.