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Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data

The stress placed on global power supply systems by the growing demand for electricity has been steadily increasing in recent years. Thus, accurate forecasting of energy demand and consumption is essential to maintain the lifestyle and economic standards of nations sustainably. However, multiple fac...

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Detalles Bibliográficos
Autores principales: Chung, Jaewon, Jang, Beakcheol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683567/
https://www.ncbi.nlm.nih.gov/pubmed/36417448
http://dx.doi.org/10.1371/journal.pone.0278071
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author Chung, Jaewon
Jang, Beakcheol
author_facet Chung, Jaewon
Jang, Beakcheol
author_sort Chung, Jaewon
collection PubMed
description The stress placed on global power supply systems by the growing demand for electricity has been steadily increasing in recent years. Thus, accurate forecasting of energy demand and consumption is essential to maintain the lifestyle and economic standards of nations sustainably. However, multiple factors, including climate change, affect the energy demands of local, national, and global power grids. Therefore, effective analysis of multivariable data is required for the accurate estimation of energy demand and consumption. In this context, some studies have suggested that LSTM and CNN models can be used to model electricity demand accurately. However, existing works have utilized training based on either electricity loads and weather observations or national metrics e.g., gross domestic product, imports, and exports. This binary segregation has degraded forecasting performance. To resolve this shortcoming, we propose a CNN-LSTM model based on a multivariable augmentation approach. Based on previous studies, we adopt 1D convolution and pooling to extract undiscovered features from temporal sequences. LSTM outperforms RNN on vanishing gradient problems while retaining its benefits regarding time-series variables. The proposed model exhibits near-perfect forecasting of electricity consumption, outperforming existing models. Further, state-level analysis and training are performed, demonstrating the utility of the proposed methodology in forecasting regional energy consumption. The proposed model outperforms other models in most areas.
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spelling pubmed-96835672022-11-24 Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data Chung, Jaewon Jang, Beakcheol PLoS One Research Article The stress placed on global power supply systems by the growing demand for electricity has been steadily increasing in recent years. Thus, accurate forecasting of energy demand and consumption is essential to maintain the lifestyle and economic standards of nations sustainably. However, multiple factors, including climate change, affect the energy demands of local, national, and global power grids. Therefore, effective analysis of multivariable data is required for the accurate estimation of energy demand and consumption. In this context, some studies have suggested that LSTM and CNN models can be used to model electricity demand accurately. However, existing works have utilized training based on either electricity loads and weather observations or national metrics e.g., gross domestic product, imports, and exports. This binary segregation has degraded forecasting performance. To resolve this shortcoming, we propose a CNN-LSTM model based on a multivariable augmentation approach. Based on previous studies, we adopt 1D convolution and pooling to extract undiscovered features from temporal sequences. LSTM outperforms RNN on vanishing gradient problems while retaining its benefits regarding time-series variables. The proposed model exhibits near-perfect forecasting of electricity consumption, outperforming existing models. Further, state-level analysis and training are performed, demonstrating the utility of the proposed methodology in forecasting regional energy consumption. The proposed model outperforms other models in most areas. Public Library of Science 2022-11-23 /pmc/articles/PMC9683567/ /pubmed/36417448 http://dx.doi.org/10.1371/journal.pone.0278071 Text en © 2022 Chung, Jang 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
Chung, Jaewon
Jang, Beakcheol
Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data
title Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data
title_full Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data
title_fullStr Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data
title_full_unstemmed Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data
title_short Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data
title_sort accurate prediction of electricity consumption using a hybrid cnn-lstm model based on multivariable data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683567/
https://www.ncbi.nlm.nih.gov/pubmed/36417448
http://dx.doi.org/10.1371/journal.pone.0278071
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