Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic

M. M. Navarro; M. N. Young; Y. T. Prasetyo; R. Nadlifatin

doi:10.1109/IEEM55944.2022.9989809

Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic

M. M. Navarro^*
, M. N. Young
, Y. T. Prasetyo
, R. Nadlifatin

^*Corresponding author for this work

Laboratory of Information System Management

Mapua University
Technological Institute of the Philippines

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

2 Citations (Scopus)

Abstract

Most of the electric distribution companies in the Philippines are interested in analyzing customer load profile, they are concerned in classifying their customer's profile into different categories based on the energy consumption, Also the user's profile will help to understand how the consumption of energy may affect the electric distribution grid. In the current condition right now, facing the COVID-19 pandemic, most Filipinos are inclined to work at home, thus the consumption of energy increased. In this paper, residential data were collected in one of the electric distribution companies in the Philippines amidst the COVID-19 pandemic conditions. The data consist of 1,048,575 customer profiles from the year 2021. This study aims to use clustering methods such as the K-means algorithm in grouping customers' profiles and validate the suitable amount of clusters using the proposed method, such as the multi-criteria model and elbow method. Results show that 2 and 7 clusters, respectively, were fitted in the data.

Original language	English
Title of host publication	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
Publisher	IEEE Computer Society
Pages	625-629
Number of pages	5
ISBN (Electronic)	9781665486873
DOIs	https://doi.org/10.1109/IEEM55944.2022.9989809
Publication status	Published - 2022
Event	2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 - Kuala Lumpur, Malaysia Duration: 7 Dec 2022 → 10 Dec 2022

Publication series

Name	IEEE International Conference on Industrial Engineering and Engineering Management
Volume	2022-December
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	7/12/22 → 10/12/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

COVID-19
K-means algorithm
load profile

Access to Document

10.1109/IEEM55944.2022.9989809

Cite this

Navarro, M. M., Young, M. N., Prasetyo, Y. T., & Nadlifatin, R. (2022). Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic. In IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 (pp. 625-629). (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2022-December). IEEE Computer Society. https://doi.org/10.1109/IEEM55944.2022.9989809

Navarro, M. M. ; Young, M. N. ; Prasetyo, Y. T. et al. / Customer Load Profile Clustering Using K-means Algorithm : A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic. IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022. IEEE Computer Society, 2022. pp. 625-629 (IEEE International Conference on Industrial Engineering and Engineering Management).

@inproceedings{36ed0f98b6af4235851465beb7e06814,

title = "Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic",

abstract = "Most of the electric distribution companies in the Philippines are interested in analyzing customer load profile, they are concerned in classifying their customer's profile into different categories based on the energy consumption, Also the user's profile will help to understand how the consumption of energy may affect the electric distribution grid. In the current condition right now, facing the COVID-19 pandemic, most Filipinos are inclined to work at home, thus the consumption of energy increased. In this paper, residential data were collected in one of the electric distribution companies in the Philippines amidst the COVID-19 pandemic conditions. The data consist of 1,048,575 customer profiles from the year 2021. This study aims to use clustering methods such as the K-means algorithm in grouping customers' profiles and validate the suitable amount of clusters using the proposed method, such as the multi-criteria model and elbow method. Results show that 2 and 7 clusters, respectively, were fitted in the data.",

keywords = "COVID-19, K-means algorithm, load profile",

author = "Navarro, \{M. M.\} and Young, \{M. N.\} and Prasetyo, \{Y. T.\} and R. Nadlifatin",

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year = "2022",

doi = "10.1109/IEEM55944.2022.9989809",

language = "English",

series = "IEEE International Conference on Industrial Engineering and Engineering Management",

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Navarro, MM, Young, MN, Prasetyo, YT & Nadlifatin, R 2022, Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic. in IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022. IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2022-December, IEEE Computer Society, pp. 625-629, 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022, Kuala Lumpur, Malaysia, 7/12/22. https://doi.org/10.1109/IEEM55944.2022.9989809

Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic. / Navarro, M. M.; Young, M. N.; Prasetyo, Y. T. et al.
IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022. IEEE Computer Society, 2022. p. 625-629 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2022-December).

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

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T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022

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AU - Young, M. N.

AU - Prasetyo, Y. T.

AU - Nadlifatin, R.

PY - 2022

Y1 - 2022

N2 - Most of the electric distribution companies in the Philippines are interested in analyzing customer load profile, they are concerned in classifying their customer's profile into different categories based on the energy consumption, Also the user's profile will help to understand how the consumption of energy may affect the electric distribution grid. In the current condition right now, facing the COVID-19 pandemic, most Filipinos are inclined to work at home, thus the consumption of energy increased. In this paper, residential data were collected in one of the electric distribution companies in the Philippines amidst the COVID-19 pandemic conditions. The data consist of 1,048,575 customer profiles from the year 2021. This study aims to use clustering methods such as the K-means algorithm in grouping customers' profiles and validate the suitable amount of clusters using the proposed method, such as the multi-criteria model and elbow method. Results show that 2 and 7 clusters, respectively, were fitted in the data.

AB - Most of the electric distribution companies in the Philippines are interested in analyzing customer load profile, they are concerned in classifying their customer's profile into different categories based on the energy consumption, Also the user's profile will help to understand how the consumption of energy may affect the electric distribution grid. In the current condition right now, facing the COVID-19 pandemic, most Filipinos are inclined to work at home, thus the consumption of energy increased. In this paper, residential data were collected in one of the electric distribution companies in the Philippines amidst the COVID-19 pandemic conditions. The data consist of 1,048,575 customer profiles from the year 2021. This study aims to use clustering methods such as the K-means algorithm in grouping customers' profiles and validate the suitable amount of clusters using the proposed method, such as the multi-criteria model and elbow method. Results show that 2 and 7 clusters, respectively, were fitted in the data.

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Navarro MM, Young MN, Prasetyo YT, Nadlifatin R. Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic. In IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022. IEEE Computer Society. 2022. p. 625-629. (IEEE International Conference on Industrial Engineering and Engineering Management). doi: 10.1109/IEEM55944.2022.9989809

Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this