Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework

Anjana Vijayan; Jung Min Yang

doi:10.1109/BCD61269.2024.10743098

Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework

Anjana Vijayan
, Jung Min Yang

Kyungpook National University

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

Abstract

The intermittent nature of distributed renewable en-ergy sources and varying patterns of end-user loads in microgrids necessitate the manufacturers to accommodate unforeseen and expected fluctuations in energy consumption and production. Lack of accurate load forecasting may result in ineffective harnessing and storage of renewable energy and complicates energy trading and dynamic pricing. Existing literature on load forecasting of microgrids is limited to single microgrids, and the possibility of inter-microgrid communication is not addressed sufficiently. This study explores the enhancement of load fore-casting in an Energy Internet (EI) framework among multiple interconnected microgrids. A novel approach is proposed which integrates k-means clustering with Support Vector Regression (SVR) to forecast the load in the EI. We also investigate the influence of the communication network of the EI in improving short-term load forecasting (STLF).

Original language	English
Title of host publication	9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024
Editors	Shun Shiramatu, Shun Okuhara, Gu Wen, Jawad Haqbeen, Motoi Iwashita, Atsushi Shimoda
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	85-90
Number of pages	6
ISBN (Electronic)	9798350394191
DOIs	https://doi.org/10.1109/BCD61269.2024.10743098
State	Published - 2024
Event	9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024 - Kitakyushu, Japan Duration: 16 Jul 2024 → 18 Jul 2024

Publication series

Name	9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

Conference

Conference	9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024
Country/Territory	Japan
City	Kitakyushu
Period	16/07/24 → 18/07/24

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Energy Internet (EI)
communication networks
load forecasting
microgrids
support vector regression

Access to Document

10.1109/BCD61269.2024.10743098

Cite this

Vijayan, A., & Yang, J. M. (2024). Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework. In S. Shiramatu, S. Okuhara, G. Wen, J. Haqbeen, M. Iwashita, & A. Shimoda (Eds.), 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024 (pp. 85-90). (9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCD61269.2024.10743098

Vijayan, Anjana ; Yang, Jung Min. / Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework. 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024. editor / Shun Shiramatu ; Shun Okuhara ; Gu Wen ; Jawad Haqbeen ; Motoi Iwashita ; Atsushi Shimoda. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 85-90 (9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024).

@inproceedings{024d7c5df4464233aea205a8047558fc,

title = "Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework",

abstract = "The intermittent nature of distributed renewable en-ergy sources and varying patterns of end-user loads in microgrids necessitate the manufacturers to accommodate unforeseen and expected fluctuations in energy consumption and production. Lack of accurate load forecasting may result in ineffective harnessing and storage of renewable energy and complicates energy trading and dynamic pricing. Existing literature on load forecasting of microgrids is limited to single microgrids, and the possibility of inter-microgrid communication is not addressed sufficiently. This study explores the enhancement of load fore-casting in an Energy Internet (EI) framework among multiple interconnected microgrids. A novel approach is proposed which integrates k-means clustering with Support Vector Regression (SVR) to forecast the load in the EI. We also investigate the influence of the communication network of the EI in improving short-term load forecasting (STLF).",

keywords = "Energy Internet (EI), communication networks, load forecasting, microgrids, support vector regression",

author = "Anjana Vijayan and Yang, \{Jung Min\}",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024 ; Conference date: 16-07-2024 Through 18-07-2024",

year = "2024",

doi = "10.1109/BCD61269.2024.10743098",

language = "English",

series = "9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "85--90",

editor = "Shun Shiramatu and Shun Okuhara and Gu Wen and Jawad Haqbeen and Motoi Iwashita and Atsushi Shimoda",

booktitle = "9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024",

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}

Vijayan, A & Yang, JM 2024, Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework. in S Shiramatu, S Okuhara, G Wen, J Haqbeen, M Iwashita & A Shimoda (eds), 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024. 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024, Institute of Electrical and Electronics Engineers Inc., pp. 85-90, 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024, Kitakyushu, Japan, 16/07/24. https://doi.org/10.1109/BCD61269.2024.10743098

Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework. / Vijayan, Anjana; Yang, Jung Min.
9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024. ed. / Shun Shiramatu; Shun Okuhara; Gu Wen; Jawad Haqbeen; Motoi Iwashita; Atsushi Shimoda. Institute of Electrical and Electronics Engineers Inc., 2024. p. 85-90 (9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024).

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

TY - GEN

T1 - Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework

AU - Vijayan, Anjana

AU - Yang, Jung Min

PY - 2024

Y1 - 2024

N2 - The intermittent nature of distributed renewable en-ergy sources and varying patterns of end-user loads in microgrids necessitate the manufacturers to accommodate unforeseen and expected fluctuations in energy consumption and production. Lack of accurate load forecasting may result in ineffective harnessing and storage of renewable energy and complicates energy trading and dynamic pricing. Existing literature on load forecasting of microgrids is limited to single microgrids, and the possibility of inter-microgrid communication is not addressed sufficiently. This study explores the enhancement of load fore-casting in an Energy Internet (EI) framework among multiple interconnected microgrids. A novel approach is proposed which integrates k-means clustering with Support Vector Regression (SVR) to forecast the load in the EI. We also investigate the influence of the communication network of the EI in improving short-term load forecasting (STLF).

AB - The intermittent nature of distributed renewable en-ergy sources and varying patterns of end-user loads in microgrids necessitate the manufacturers to accommodate unforeseen and expected fluctuations in energy consumption and production. Lack of accurate load forecasting may result in ineffective harnessing and storage of renewable energy and complicates energy trading and dynamic pricing. Existing literature on load forecasting of microgrids is limited to single microgrids, and the possibility of inter-microgrid communication is not addressed sufficiently. This study explores the enhancement of load fore-casting in an Energy Internet (EI) framework among multiple interconnected microgrids. A novel approach is proposed which integrates k-means clustering with Support Vector Regression (SVR) to forecast the load in the EI. We also investigate the influence of the communication network of the EI in improving short-term load forecasting (STLF).

KW - Energy Internet (EI)

KW - communication networks

KW - load forecasting

KW - microgrids

KW - support vector regression

UR - https://www.scopus.com/pages/publications/85211391721

U2 - 10.1109/BCD61269.2024.10743098

DO - 10.1109/BCD61269.2024.10743098

M3 - Conference contribution

AN - SCOPUS:85211391721

T3 - 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

SP - 85

EP - 90

BT - 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

A2 - Shiramatu, Shun

A2 - Okuhara, Shun

A2 - Wen, Gu

A2 - Haqbeen, Jawad

A2 - Iwashita, Motoi

A2 - Shimoda, Atsushi

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

Y2 - 16 July 2024 through 18 July 2024

ER -

Vijayan A, Yang JM. Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework. In Shiramatu S, Okuhara S, Wen G, Haqbeen J, Iwashita M, Shimoda A, editors, 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024. Institute of Electrical and Electronics Engineers Inc. 2024. p. 85-90. (9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024). doi: 10.1109/BCD61269.2024.10743098

Enhancing Load Forecasting by Clustering in Distributed Microgrids Based Energy Internet Framework

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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