Machine-learning-integrated load scheduling for peak electricity reduction

Minyoung Sung; Younghoo Ko

doi:10.1109/ICCE.2015.7066425

Machine-learning-integrated load scheduling for peak electricity reduction

Minyoung Sung, Younghoo Ko

Department of Mechanical and Information Engineering

University of Seoul

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The scheduling of household electrical loads can contribute to a significant reduction in peak demand. This paper introduces a load scheduling scheme that integrates an SVM (Support Vector Machine) model for demand prediction. The experiment results confirm the strength of the proposed scheme, showing its ability to achieve the intended performance in consideration of the trade-off among peak reduction, temperature band violation, and switch count.

Original language	English
Title of host publication	2015 IEEE International Conference on Consumer Electronics, ICCE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	309-310
Number of pages	2
ISBN (Electronic)	9781479975426
DOIs	https://doi.org/10.1109/ICCE.2015.7066425
State	Published - 23 Mar 2015
Event	2015 IEEE International Conference on Consumer Electronics, ICCE 2015 - Las Vegas, United States Duration: 9 Jan 2015 → 12 Jan 2015

Publication series

Name	2015 IEEE International Conference on Consumer Electronics, ICCE 2015

Conference

Conference	2015 IEEE International Conference on Consumer Electronics, ICCE 2015
Country/Territory	United States
City	Las Vegas
Period	9/01/15 → 12/01/15

Access to Document

10.1109/ICCE.2015.7066425

Cite this

Sung, M., & Ko, Y. (2015). Machine-learning-integrated load scheduling for peak electricity reduction. In 2015 IEEE International Conference on Consumer Electronics, ICCE 2015 (pp. 309-310). Article 7066425 (2015 IEEE International Conference on Consumer Electronics, ICCE 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2015.7066425

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title = "Machine-learning-integrated load scheduling for peak electricity reduction",

abstract = "The scheduling of household electrical loads can contribute to a significant reduction in peak demand. This paper introduces a load scheduling scheme that integrates an SVM (Support Vector Machine) model for demand prediction. The experiment results confirm the strength of the proposed scheme, showing its ability to achieve the intended performance in consideration of the trade-off among peak reduction, temperature band violation, and switch count.",

author = "Minyoung Sung and Younghoo Ko",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Conference on Consumer Electronics, ICCE 2015 ; Conference date: 09-01-2015 Through 12-01-2015",

year = "2015",

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doi = "10.1109/ICCE.2015.7066425",

language = "English",

series = "2015 IEEE International Conference on Consumer Electronics, ICCE 2015",

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Sung, M & Ko, Y 2015, Machine-learning-integrated load scheduling for peak electricity reduction. in 2015 IEEE International Conference on Consumer Electronics, ICCE 2015., 7066425, 2015 IEEE International Conference on Consumer Electronics, ICCE 2015, Institute of Electrical and Electronics Engineers Inc., pp. 309-310, 2015 IEEE International Conference on Consumer Electronics, ICCE 2015, Las Vegas, United States, 9/01/15. https://doi.org/10.1109/ICCE.2015.7066425

Machine-learning-integrated load scheduling for peak electricity reduction. / Sung, Minyoung; Ko, Younghoo.
2015 IEEE International Conference on Consumer Electronics, ICCE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 309-310 7066425 (2015 IEEE International Conference on Consumer Electronics, ICCE 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Machine-learning-integrated load scheduling for peak electricity reduction

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