RNN-based Deep Learning for One-hour ahead Load Forecasting

Van Bui; Van Hoa Nguyen; Tung Lam Pham; Joongheon Kim; Yeong Min Jang

doi:10.1109/ICAIIC48513.2020.9065071

RNN-based Deep Learning for One-hour ahead Load Forecasting

Van Bui
, Van Hoa Nguyen
, Tung Lam Pham
, Joongheon Kim
, Yeong Min Jang

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

22 Citations (Scopus)

Abstract

This paper is focusing on one hour-ahead forecasting on Power Load, using Recurrent Neural Network based scheme. This study only uses the generated data of Kookmin University's Load, so it required a considerable number of resources for forecasting. Multi-scaled RNN model was proposed for the Load Forecasting, which is suitable for both short term and long term memory.

Original language	English
Title of host publication	2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	587-589
Number of pages	3
ISBN (Electronic)	9781728149851
DOIs	https://doi.org/10.1109/ICAIIC48513.2020.9065071
Publication status	Published - 2020 Feb
Event	2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan Duration: 2020 Feb 19 → 2020 Feb 21

Publication series

Name	2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Conference

Conference	2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Country/Territory	Japan
City	Fukuoka
Period	20/2/19 → 20/2/21

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Machine Learning
Output Load Forecasting
Recurrent Neural Network

ASJC Scopus subject areas

Information Systems and Management
Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Information Systems
Signal Processing

Access to Document

10.1109/ICAIIC48513.2020.9065071

Cite this

Bui, V., Nguyen, V. H., Pham, T. L., Kim, J., & Jang, Y. M. (2020). RNN-based Deep Learning for One-hour ahead Load Forecasting. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 (pp. 587-589). Article 9065071 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC48513.2020.9065071

RNN-based Deep Learning for One-hour ahead Load Forecasting. / Bui, Van; Nguyen, Van Hoa; Pham, Tung Lam et al.
2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 587-589 9065071 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

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

Bui, V, Nguyen, VH, Pham, TL, Kim, J & Jang, YM 2020, RNN-based Deep Learning for One-hour ahead Load Forecasting. in 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020., 9065071, 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 587-589, 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, 20/2/19. https://doi.org/10.1109/ICAIIC48513.2020.9065071

Bui V, Nguyen VH, Pham TL, Kim J, Jang YM. RNN-based Deep Learning for One-hour ahead Load Forecasting. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 587-589. 9065071. (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). doi: 10.1109/ICAIIC48513.2020.9065071

Bui, Van ; Nguyen, Van Hoa ; Pham, Tung Lam et al. / RNN-based Deep Learning for One-hour ahead Load Forecasting. 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 587-589 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

@inproceedings{329f37e192364019a0a41c32f72bfe6a,

title = "RNN-based Deep Learning for One-hour ahead Load Forecasting",

abstract = "This paper is focusing on one hour-ahead forecasting on Power Load, using Recurrent Neural Network based scheme. This study only uses the generated data of Kookmin University's Load, so it required a considerable number of resources for forecasting. Multi-scaled RNN model was proposed for the Load Forecasting, which is suitable for both short term and long term memory.",

keywords = "Machine Learning, Output Load Forecasting, Recurrent Neural Network",

author = "Van Bui and Nguyen, \{Van Hoa\} and Pham, \{Tung Lam\} and Joongheon Kim and Jang, \{Yeong Min\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 ; Conference date: 19-02-2020 Through 21-02-2020",

year = "2020",

month = feb,

doi = "10.1109/ICAIIC48513.2020.9065071",

language = "English",

series = "2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020",

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

pages = "587--589",

booktitle = "2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020",

}

TY - GEN

T1 - RNN-based Deep Learning for One-hour ahead Load Forecasting

AU - Bui, Van

AU - Nguyen, Van Hoa

AU - Pham, Tung Lam

AU - Kim, Joongheon

AU - Jang, Yeong Min

PY - 2020/2

Y1 - 2020/2

N2 - This paper is focusing on one hour-ahead forecasting on Power Load, using Recurrent Neural Network based scheme. This study only uses the generated data of Kookmin University's Load, so it required a considerable number of resources for forecasting. Multi-scaled RNN model was proposed for the Load Forecasting, which is suitable for both short term and long term memory.

AB - This paper is focusing on one hour-ahead forecasting on Power Load, using Recurrent Neural Network based scheme. This study only uses the generated data of Kookmin University's Load, so it required a considerable number of resources for forecasting. Multi-scaled RNN model was proposed for the Load Forecasting, which is suitable for both short term and long term memory.

KW - Machine Learning

KW - Output Load Forecasting

KW - Recurrent Neural Network

UR - https://www.scopus.com/pages/publications/85084073397

U2 - 10.1109/ICAIIC48513.2020.9065071

DO - 10.1109/ICAIIC48513.2020.9065071

M3 - Conference contribution

AN - SCOPUS:85084073397

T3 - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

SP - 587

EP - 589

BT - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Y2 - 19 February 2020 through 21 February 2020

ER -

RNN-based Deep Learning for One-hour ahead Load Forecasting

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this