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Disaggregated End-Use Energy Sensing for the Smart Grid

Published: 01 January 2011 Publication History

Abstract

Most energy meters installed by utilities are intended primarily to support billing functions. Meters report only the aggregate energy consumption of a home or business over intervals as long as a month. In contrast, disaggregated energy usage data identified by individual devices or appliances offers a much more descriptive dataset that has the potential to inform and empower a wide variety of energy stakeholders, from homeowners and building operators to utilities and policy makers. In this article, the authors survey existing and emerging disaggregation techniques and highlight signal features that might be used to sense disaggregated data in a viable and cost-effective manner. They provide a summary of a new approach to electrical load disaggregation that uses voltage noise, including a brief overview of their sensing hardware, classification algorithms, and evaluation in 14 homes. The article concludes with a discussion of current open research problems that must be addressed before disaggregated energy sensing can be widely deployed.

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  • (2024)Wasted Energy? Illuminating Energy Data With OntologiesIEEE Pervasive Computing10.1109/MPRV.2024.338467323:2(27-37)Online publication date: 1-Apr-2024
  • (2024)Design and implementation of a low-cost power logger device for specific demand profile analysis in demand-side management studies for smart gridsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121888238:PAOnline publication date: 15-Mar-2024
  • (2022)Non-intrusive load monitoring method with inception structured CNNApplied Intelligence10.1007/s10489-021-02690-y52:6(6227-6244)Online publication date: 1-Apr-2022
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Information & Contributors

Information

Published In

cover image IEEE Pervasive Computing
IEEE Pervasive Computing  Volume 10, Issue 1
January 2011
96 pages

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 January 2011

Author Tags

  1. Disaggregated energy sensing
  2. Disaggregated energy sensing, Sustainability, Electricity, Water, Gas, Smart grid
  3. Electricity
  4. Gas
  5. Smart grid
  6. Sustainability
  7. Water

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Cited By

View all
  • (2024)Wasted Energy? Illuminating Energy Data With OntologiesIEEE Pervasive Computing10.1109/MPRV.2024.338467323:2(27-37)Online publication date: 1-Apr-2024
  • (2024)Design and implementation of a low-cost power logger device for specific demand profile analysis in demand-side management studies for smart gridsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121888238:PAOnline publication date: 15-Mar-2024
  • (2022)Non-intrusive load monitoring method with inception structured CNNApplied Intelligence10.1007/s10489-021-02690-y52:6(6227-6244)Online publication date: 1-Apr-2022
  • (2021)Deep Learning-Based Energy Disaggregation and On/Off Detection of Household AppliancesACM Transactions on Knowledge Discovery from Data10.1145/344130015:3(1-21)Online publication date: 3-May-2021
  • (2021)A novel DNN-HMM-based approach for extracting single loads from aggregate power signals2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2016.7472104(2384-2388)Online publication date: 11-Mar-2021
  • (2020)Office Appliances Identification and Monitoring using Deep Leaning based Energy Disaggregation for Smart BuildingsIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society10.1109/IECON43393.2020.9255127(1986-1991)Online publication date: 18-Oct-2020
  • (2019)Learning from Sets of Items in Recommender SystemsACM Transactions on Interactive Intelligent Systems10.1145/33261289:4(1-26)Online publication date: 25-Jul-2019
  • (2019)Structured Dictionary Learning for Energy DisaggregationProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3328301(24-34)Online publication date: 15-Jun-2019
  • (2019)A survey of privacy preserving schemes in IoE enabled Smart Grid Advanced Metering InfrastructureCluster Computing10.1007/s10586-018-2820-922:1(43-69)Online publication date: 1-Mar-2019
  • (2019)Machine learning approaches for non-intrusive load monitoringArtificial Intelligence Review10.1007/s10462-018-9613-752:1(217-243)Online publication date: 1-Jun-2019
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