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Multi-region electricity demand prediction with ensemble deep neural networks
Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utili...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10194882/ https://www.ncbi.nlm.nih.gov/pubmed/37200368 http://dx.doi.org/10.1371/journal.pone.0285456 |
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author | Irfan, Muhammad Shaf, Ahmad Ali, Tariq Zafar, Mariam Rahman, Saifur Mursal, Salim Nasar Faraj AlThobiani, Faisal A. Almas, Majid Attar, H. M. Abdussamiee, Nagi |
author_facet | Irfan, Muhammad Shaf, Ahmad Ali, Tariq Zafar, Mariam Rahman, Saifur Mursal, Salim Nasar Faraj AlThobiani, Faisal A. Almas, Majid Attar, H. M. Abdussamiee, Nagi |
author_sort | Irfan, Muhammad |
collection | PubMed |
description | Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R(2)), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption. |
format | Online Article Text |
id | pubmed-10194882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101948822023-05-19 Multi-region electricity demand prediction with ensemble deep neural networks Irfan, Muhammad Shaf, Ahmad Ali, Tariq Zafar, Mariam Rahman, Saifur Mursal, Salim Nasar Faraj AlThobiani, Faisal A. Almas, Majid Attar, H. M. Abdussamiee, Nagi PLoS One Research Article Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R(2)), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption. Public Library of Science 2023-05-18 /pmc/articles/PMC10194882/ /pubmed/37200368 http://dx.doi.org/10.1371/journal.pone.0285456 Text en © 2023 Irfan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Irfan, Muhammad Shaf, Ahmad Ali, Tariq Zafar, Mariam Rahman, Saifur Mursal, Salim Nasar Faraj AlThobiani, Faisal A. Almas, Majid Attar, H. M. Abdussamiee, Nagi Multi-region electricity demand prediction with ensemble deep neural networks |
title | Multi-region electricity demand prediction with ensemble deep neural networks |
title_full | Multi-region electricity demand prediction with ensemble deep neural networks |
title_fullStr | Multi-region electricity demand prediction with ensemble deep neural networks |
title_full_unstemmed | Multi-region electricity demand prediction with ensemble deep neural networks |
title_short | Multi-region electricity demand prediction with ensemble deep neural networks |
title_sort | multi-region electricity demand prediction with ensemble deep neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10194882/ https://www.ncbi.nlm.nih.gov/pubmed/37200368 http://dx.doi.org/10.1371/journal.pone.0285456 |
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