Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances

Samira Ghorbanpour; Rammohan Mallipeddi

doi:10.1109/SMC.2018.00709

Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances

Samira Ghorbanpour
, Rammohan Mallipeddi

School of Electronics Engineering

Kyungpook National University

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

4 Scopus citations

Abstract

In non-intrusive load monitoring, identification of electrical loads based on single point measurement of different energy related parameters plays a significant role. In literature, different conventional features such as true power, reactive power, RMS voltage, RMS current, phase angle and frequency in addition to the non-conventional features were employed. In addition, a variety of classifiers such as k-nearest neighbors (k-NN), support vector machine (SVM), random forest and Gaussian mixture models (GMM) have been employed. In this paper, we demonstrate that the classification performance strongly depends on the classifier and associated features selected. The experiments are performed on ACS-F2 Database of Appliance Consumption Signatures consisting of 225 devices belonging to 15 different categories.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4184-4189
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00709
State	Published - 2 Jul 2018
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

appliance identification
appliance load monitoring
feature selection
non-intrusive load monitoring

Access to Document

10.1109/SMC.2018.00709

Cite this

Ghorbanpour, S., & Mallipeddi, R. (2018). Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 4184-4189). Article 8616706 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00709

Ghorbanpour, Samira ; Mallipeddi, Rammohan. / Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 4184-4189 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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title = "Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances",

abstract = "In non-intrusive load monitoring, identification of electrical loads based on single point measurement of different energy related parameters plays a significant role. In literature, different conventional features such as true power, reactive power, RMS voltage, RMS current, phase angle and frequency in addition to the non-conventional features were employed. In addition, a variety of classifiers such as k-nearest neighbors (k-NN), support vector machine (SVM), random forest and Gaussian mixture models (GMM) have been employed. In this paper, we demonstrate that the classification performance strongly depends on the classifier and associated features selected. The experiments are performed on ACS-F2 Database of Appliance Consumption Signatures consisting of 225 devices belonging to 15 different categories.",

keywords = "appliance identification, appliance load monitoring, feature selection, non-intrusive load monitoring",

author = "Samira Ghorbanpour and Rammohan Mallipeddi",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

year = "2018",

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doi = "10.1109/SMC.2018.00709",

language = "English",

series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

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Ghorbanpour, S & Mallipeddi, R 2018, Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616706, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 4184-4189, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00709

Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances. / Ghorbanpour, Samira; Mallipeddi, Rammohan.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 4184-4189 8616706 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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

TY - GEN

T1 - Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances

AU - Ghorbanpour, Samira

AU - Mallipeddi, Rammohan

PY - 2018/7/2

Y1 - 2018/7/2

N2 - In non-intrusive load monitoring, identification of electrical loads based on single point measurement of different energy related parameters plays a significant role. In literature, different conventional features such as true power, reactive power, RMS voltage, RMS current, phase angle and frequency in addition to the non-conventional features were employed. In addition, a variety of classifiers such as k-nearest neighbors (k-NN), support vector machine (SVM), random forest and Gaussian mixture models (GMM) have been employed. In this paper, we demonstrate that the classification performance strongly depends on the classifier and associated features selected. The experiments are performed on ACS-F2 Database of Appliance Consumption Signatures consisting of 225 devices belonging to 15 different categories.

AB - In non-intrusive load monitoring, identification of electrical loads based on single point measurement of different energy related parameters plays a significant role. In literature, different conventional features such as true power, reactive power, RMS voltage, RMS current, phase angle and frequency in addition to the non-conventional features were employed. In addition, a variety of classifiers such as k-nearest neighbors (k-NN), support vector machine (SVM), random forest and Gaussian mixture models (GMM) have been employed. In this paper, we demonstrate that the classification performance strongly depends on the classifier and associated features selected. The experiments are performed on ACS-F2 Database of Appliance Consumption Signatures consisting of 225 devices belonging to 15 different categories.

KW - appliance identification

KW - appliance load monitoring

KW - feature selection

KW - non-intrusive load monitoring

UR - https://www.scopus.com/pages/publications/85062213350

U2 - 10.1109/SMC.2018.00709

DO - 10.1109/SMC.2018.00709

M3 - Conference contribution

AN - SCOPUS:85062213350

T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

SP - 4184

EP - 4189

BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Y2 - 7 October 2018 through 10 October 2018

ER -

Ghorbanpour S, Mallipeddi R. Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4184-4189. 8616706. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). doi: 10.1109/SMC.2018.00709

Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances

Abstract

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UN SDGs

Keywords

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