research-article

A Comprehensive Feature Study for Appliance Recognition on High Frequency Energy Data

Authors:

Thomas Kriechbaumer,

Hans-Arno JacobsenAuthors Info & Claims

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

Pages 121 - 131

https://doi.org/10.1145/3077839.3077845

Published: 16 May 2017 Publication History

Abstract

Awareness about the energy consumption of appliances can help to save energy in households. Non-intrusive Load Monitoring (NILM) is a feasible approach to provide consumption feedback at appliance level. In this paper, we evaluate a broad set of features for electrical appliance recognition, extracted from high frequency start-up events. These evaluations were applied on several existing high frequency energy datasets. To examine clean signatures, we ran all experiments on two datasets that are based on isolated appliance events; more realistic results were retrieved from two real household datasets. Our feature set consists of 36 signatures from related work including novel approaches, and from other research fields. The results of this work include a stand-alone feature ranking, promising feature combinations for appliance recognition in general and appliance-wise performances.

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Cited By

Rong JWang CZhou QHe YWu H(2025)Enhancing Non-Intrusive Load Monitoring Through Transfer Learning with Transformer ModelsEnergy and Buildings10.1016/j.enbuild.2025.115334(115334)Online publication date: Jan-2025
https://doi.org/10.1016/j.enbuild.2025.115334
Xiao JTan MPan RSu YLi TXu Z(2024)Nonintrusive Load Identification Considering Unknown Load Based on Bimodal Fusion and One-Class ClassificationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.334382973(1-11)Online publication date: 2024
https://doi.org/10.1109/TIM.2023.3343829
Petralia ACharpentier PPalpanas T(2023)ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption SeriesProceedings of the VLDB Endowment10.14778/3632093.363211517:3(553-562)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.14778/3632093.3632115
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A Comprehensive Feature Study for Appliance Recognition on High Frequency Energy Data
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cover image ACM Conferences

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

May 2017

388 pages

ISBN:9781450350365

DOI:10.1145/3077839

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2017

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Bundesministerium für Wirtschaft und Energie
Alexander von Humboldt-Stiftung

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e-Energy '17

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SIGCOMM

e-Energy '17: The Eighth International Conference on Future Energy Systems

May 16 - 19, 2017

Shatin, Hong Kong

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Overall Acceptance Rate 160 of 446 submissions, 36%

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The 16th ACM International Conference on Future and Sustainable Energy Systems

June 17 - 20, 2025

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Cited By

Rong JWang CZhou QHe YWu H(2025)Enhancing Non-Intrusive Load Monitoring Through Transfer Learning with Transformer ModelsEnergy and Buildings10.1016/j.enbuild.2025.115334(115334)Online publication date: Jan-2025
https://doi.org/10.1016/j.enbuild.2025.115334
Xiao JTan MPan RSu YLi TXu Z(2024)Nonintrusive Load Identification Considering Unknown Load Based on Bimodal Fusion and One-Class ClassificationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.334382973(1-11)Online publication date: 2024
https://doi.org/10.1109/TIM.2023.3343829
Petralia ACharpentier PPalpanas T(2023)ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption SeriesProceedings of the VLDB Endowment10.14778/3632093.363211517:3(553-562)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.14778/3632093.3632115
Petralia ACharpentier PBoniol PPalpanas T(2023)Appliance Detection Using Very Low-Frequency Smart Meter Time SeriesProceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3575813.3595198(214-225)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3575813.3595198
Schirmer PMporas I(2023)Non-Intrusive Load Monitoring: A ReviewIEEE Transactions on Smart Grid10.1109/TSG.2022.318959814:1(769-784)Online publication date: Jan-2023
https://doi.org/10.1109/TSG.2022.3189598
Kamyshev ITerzija VGryazina E(2023)Edframe: Open-Source Library for End-to-End Energy Disaggregation in Python2023 IEEE Belgrade PowerTech10.1109/PowerTech55446.2023.10202926(01-07)Online publication date: 25-Jun-2023
https://doi.org/10.1109/PowerTech55446.2023.10202926
Li YWang HYang ZYang JChen Z(2023)Stacking Ensemble Learning-Based Load Identification Considering Feature Fusion by Cyber-Physical ApproachIEEE Sensors Journal10.1109/JSEN.2023.324100023:6(5997-6007)Online publication date: 15-Mar-2023
https://doi.org/10.1109/JSEN.2023.3241000
Schwermer RBuchberger JMayer RJacobsen HLehnhoff SIrwin DWang D(2022)Federated office plug-load identification for building management systemsProceedings of the Thirteenth ACM International Conference on Future Energy Systems10.1145/3538637.3538845(114-126)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3538637.3538845
Isenko AMayer RJedele JJacobsen HIves ZBonifati AEl Abbadi A(2022)Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing PipelinesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517848(1825-1839)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517848
Chea RThourn KChhorn S(2022)Improving V-I Trajectory Load Signature in NILM Approach2022 International Electrical Engineering Congress (iEECON)10.1109/iEECON53204.2022.9741688(1-4)Online publication date: 9-Mar-2022
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