Detection and characterization of domestic heat pumps

Guillaume Le Ray, Morten Herget Christensen, Pierre Pinson

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Smart meters allow utilities to gain access to tremendous amount of metering data, from which they can improve their knowledge on customers. In this paper, we propose a method to detect and characterize domestic heat pumps from household power consumption data. Appliance detection is not a trivial task, owing to the variability in heat pump load dynamics and to the distortion of their power consumption signature by other appliances. Compared to State-of-the-art methodologies that relies on energy disaggregation with supervised learning and high-resolution data (e.g., 10 seconds), our novel approach uses lower resolution data. Also, it is semi-supervised and relies on a Bayesian framework allowing to continuously learn as new data becomes available. To do so, the overall power consumption signals are decomposed and approximated into a dictionary of boxcar functions using sparse signal approximation to isolate heat pumps activity. The learning phase consists then to generate distributions summarizing power consumption, operation time and frequency of heat pumps activation events summarized as boxcar functions after approximation. During the test phase, the distributions are used as prior to calculate the likelihood that a boxcar function is generated by a heat pump. Using standard classification performance measure and an application to data from the EcoGrid EU project, the methodology reaches high performance in heat pump detection.

    Original languageEnglish
    Title of host publicationProceedings of 2019 IEEE Milan PowerTech
    Number of pages6
    PublisherIEEE
    Publication date1 Jun 2019
    Article number8810930
    ISBN (Electronic)9781538647226
    DOIs
    Publication statusPublished - 1 Jun 2019
    Event2019 IEEE Milan PowerTech - Milan, Italy
    Duration: 23 Jun 201927 Jun 2019
    Conference number: 13
    https://ieeexplore.ieee.org/xpl/conhome/8792346/proceeding

    Conference

    Conference2019 IEEE Milan PowerTech
    Number13
    Country/TerritoryItaly
    CityMilan
    Period23/06/201927/06/2019
    Internet address
    Series2019 IEEE Milan PowerTech, PowerTech 2019

    Keywords

    • Bayesian framework
    • Energy analytics
    • Heat pump detection
    • Semi-supervised learning

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