Energy disaggregation for real-time building flexibility detection

Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    14 Citations (Scopus)
    31 Downloads (Pure)

    Abstract

    Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy.

    Original languageEnglish
    Title of host publication2016 IEEE Power and Energy Society General Meeting, PESGM 2016
    Subtitle of host publication17-21 July 2016, Boston, Massachusetts
    Place of PublicationPiscataway, NJ
    PublisherIEEE Computer Society
    ISBN (Electronic)978-1-5090-4168-8
    DOIs
    Publication statusPublished - 10 Nov 2016
    EventIEEE Power and Energy Society General Meeting, PESGM 2016: Paving the way for grid modernization - Boston, United States
    Duration: 17 Jul 201621 Jul 2017
    http://www.pes-gm.org/2016/

    Publication series

    NameIEEE Power and Energy Society General Meeting
    PublisherIEEE
    Volume2016
    ISSN (Print)1944-9925
    ISSN (Electronic)1944-9933

    Conference

    ConferenceIEEE Power and Energy Society General Meeting, PESGM 2016
    Abbreviated titlePESGM
    CountryUnited States
    CityBoston
    Period17/07/1621/07/17
    Internet address

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