research-article

Plug Load Identification using Regression based Nearest Neighbor Classifier

Authors:

Raghunath Reddy,

Niranjan Keesara,

Vikram PudiAuthors Info & Claims

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

Pages 101 - 110

https://doi.org/10.1145/3077839.3077853

Published: 16 May 2017 Publication History

Abstract

Energy utilization can be improved by precise plug load monitoring and control. Plug load energy consumption is nearly 30% of the total building energy consumption. Therefore, plug load identification is a key requirement for energy conservation in buildings. Intrusive load monitoring techniques identify loads precisely but have not been tested widely so far for their performance in changing operating conditions. Hence, the present research proposes a robust low frequency intrusive load monitoring technique to identify load accurately. A smart power strip using proposed load identification technique is designed and developed. Linear regression is applied on the acquired data to capture the behavioral trends of a particular device more explicitly and concisely. Further, weighted K-NN classifier is applied on the transformed data set for device. Experimental results show that the proposed algorithm performs better than the standard classifiers, and can offer tangible savings.

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Cited By

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

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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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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

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Cited By

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

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