Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue
This article aims to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each poi...
Published in: | Indonesian Journal of Electrical Engineering and Computer Science |
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Institute of Advanced Engineering and Science
2022
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Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126718341&doi=10.11591%2fijeecs.v26.i1.pp56-66&partnerID=40&md5=a4f51697b174bf90cc772e186d787314 |
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2-s2.0-85126718341 Hussain M.M.; Zakaria Z.; Dahlan N.Y.; Ilham N.I.; Hussin Z.; Rahman N.H.A.; Yasin M.A.M. Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue 2022 Indonesian Journal of Electrical Engineering and Computer Science 26 1 10.11591/ijeecs.v26.i1.pp56-66 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126718341&doi=10.11591%2fijeecs.v26.i1.pp56-66&partnerID=40&md5=a4f51697b174bf90cc772e186d787314 This article aims to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each point. Nowadays, utility providers claim that natural sources are used to generate power by rising consumer demands for energy. However, occasionally utility workers need to know the demand at certain location, corresponding to maintenance issues or for any shutdown area involved. A distribution pattern based on the data can be predicted based on the incoming data profile without having detailed information of certain load bus, the concept of derivatives was relevant to forecast the types of distribution data. The model was constructed with load profile information based on three different locations, and the concept of derivative was recognized, including the type of incoming data. Historical data were captured from a selected location in Malaysia that was proposed to train the JADE algorithm from three different empirical distributions of consumers, recording every 15 minutes per day. The results were analyzed based on the error measurement and compared with the real specific load distribution feeder information of needed profiles. © 2022 Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 25024752 English Article All Open Access; Gold Open Access |
author |
Hussain M.M.; Zakaria Z.; Dahlan N.Y.; Ilham N.I.; Hussin Z.; Rahman N.H.A.; Yasin M.A.M. |
spellingShingle |
Hussain M.M.; Zakaria Z.; Dahlan N.Y.; Ilham N.I.; Hussin Z.; Rahman N.H.A.; Yasin M.A.M. Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue |
author_facet |
Hussain M.M.; Zakaria Z.; Dahlan N.Y.; Ilham N.I.; Hussin Z.; Rahman N.H.A.; Yasin M.A.M. |
author_sort |
Hussain M.M.; Zakaria Z.; Dahlan N.Y.; Ilham N.I.; Hussin Z.; Rahman N.H.A.; Yasin M.A.M. |
title |
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue |
title_short |
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue |
title_full |
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue |
title_fullStr |
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue |
title_full_unstemmed |
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue |
title_sort |
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue |
publishDate |
2022 |
container_title |
Indonesian Journal of Electrical Engineering and Computer Science |
container_volume |
26 |
container_issue |
1 |
doi_str_mv |
10.11591/ijeecs.v26.i1.pp56-66 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126718341&doi=10.11591%2fijeecs.v26.i1.pp56-66&partnerID=40&md5=a4f51697b174bf90cc772e186d787314 |
description |
This article aims to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each point. Nowadays, utility providers claim that natural sources are used to generate power by rising consumer demands for energy. However, occasionally utility workers need to know the demand at certain location, corresponding to maintenance issues or for any shutdown area involved. A distribution pattern based on the data can be predicted based on the incoming data profile without having detailed information of certain load bus, the concept of derivatives was relevant to forecast the types of distribution data. The model was constructed with load profile information based on three different locations, and the concept of derivative was recognized, including the type of incoming data. Historical data were captured from a selected location in Malaysia that was proposed to train the JADE algorithm from three different empirical distributions of consumers, recording every 15 minutes per day. The results were analyzed based on the error measurement and compared with the real specific load distribution feeder information of needed profiles. © 2022 Institute of Advanced Engineering and Science. All rights reserved. |
publisher |
Institute of Advanced Engineering and Science |
issn |
25024752 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809678480087973888 |