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, PowerTech 2019 - Milan, Italy
Duration: 23 Jun 201927 Jun 2019

Conference

Conference2019 IEEE Milan PowerTech, PowerTech 2019
CountryItaly
CityMilan
Period23/06/201927/06/2019
Series2019 IEEE Milan PowerTech, PowerTech 2019

Keywords

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

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