Activity detection and modeling using smart meter data: concept and case studies

Hao Wang, Gonzague Henri, Chin-Woo Tan, Ram Rajagopal

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

1 Citation (Scopus)

Abstract

Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure. There has been a growing research interest toward appliance load disaggregation via nonintrusive load monitoring. As the electricity consumption of appliances is directly associated with the activities of consumers, this paper proposes a new and more effective approach, i.e., activity disaggregation. We present the concept of activity disaggregation and discuss its advantage over traditional appliance load disaggregation. We develop a framework by leverage machine learning for activity detection based on residential load data and features. We show through numerical case studies to demonstrate the effectiveness of the activity detection method and analyze consumer behaviors by time-dependent activity modeling. Last but not least, we discuss some potential use cases that can benefit from activity disaggregation and some future research directions.

Original languageEnglish
Title of host publication2020 IEEE Power and Energy Society General Meeting, PESGM 2020
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781728155081
ISBN (Print)9781728155098
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventIEEE Power and Energy Society General Meeting 2020 - Virtual9, Montreal, Canada
Duration: 3 Aug 20206 Aug 2020
https://pes-gm.org/2020/ (Website)
https://ieeexplore.ieee.org/xpl/conhome/9281379/proceeding (Proceedings)

Publication series

NameIEEE Power and Energy Society General Meeting
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2020-August
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

ConferenceIEEE Power and Energy Society General Meeting 2020
Abbreviated titlePES-GM 2020
CountryCanada
CityMontreal
Period3/08/206/08/20
Internet address

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