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Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach
The aim of this research is to forecast seasonal fluctuations in electricity consumption, and electricity usage efficiency of industrial sectors and identify the impacts of the novel coronavirus disease 2019 (COVID-19). For this purpose, a new seasonal grey prediction model (AWBO-DGGM(1,1)) is propo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758556/ https://www.ncbi.nlm.nih.gov/pubmed/36570723 http://dx.doi.org/10.1016/j.energy.2021.119952 |
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author | Chen, Hai-Bao Pei, Ling-Ling Zhao, Yu-Feng |
author_facet | Chen, Hai-Bao Pei, Ling-Ling Zhao, Yu-Feng |
author_sort | Chen, Hai-Bao |
collection | PubMed |
description | The aim of this research is to forecast seasonal fluctuations in electricity consumption, and electricity usage efficiency of industrial sectors and identify the impacts of the novel coronavirus disease 2019 (COVID-19). For this purpose, a new seasonal grey prediction model (AWBO-DGGM(1,1)) is proposed: it combines buffer operators and the DGGM(1,1) model. Based on the quarterly data of the industrial enterprises in Zhejiang Province of China from the first quarter of 2013 to the first quarter of 2020, the GM(1,1), DGGM(1,1), SVM, and AWBO-DGGM(1,1) models are employed, respectively, to simulate and forecast seasonal variations in electricity consumption, the added value, and electricity usage efficiency. The results indicate that the AWBO-DGGM(1,1) models can identify seasonal fluctuations and variations in time series data, and predict the impact of COVID-19 on industrial systems. The minimum mean absolute percentage errors (MAPEs) of the electricity consumption, added value, and electricity usage efficiency of industrial enterprises separately are 0.12%, 0.10%, and 3.01% in the training stage, while those in the test stage are 6.79%, 4.09%, and 2.25%, respectively. The electricity consumption, added value, and electricity usage efficiency of industrial enterprises in Zhejiang Province will still present a tendency to grow with seasonal fluctuations from 2020 to 2022. Of them, the added value is predicted to increase the fastest, followed by electricity consumption. |
format | Online Article Text |
id | pubmed-9758556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97585562022-12-19 Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach Chen, Hai-Bao Pei, Ling-Ling Zhao, Yu-Feng Energy (Oxf) Article The aim of this research is to forecast seasonal fluctuations in electricity consumption, and electricity usage efficiency of industrial sectors and identify the impacts of the novel coronavirus disease 2019 (COVID-19). For this purpose, a new seasonal grey prediction model (AWBO-DGGM(1,1)) is proposed: it combines buffer operators and the DGGM(1,1) model. Based on the quarterly data of the industrial enterprises in Zhejiang Province of China from the first quarter of 2013 to the first quarter of 2020, the GM(1,1), DGGM(1,1), SVM, and AWBO-DGGM(1,1) models are employed, respectively, to simulate and forecast seasonal variations in electricity consumption, the added value, and electricity usage efficiency. The results indicate that the AWBO-DGGM(1,1) models can identify seasonal fluctuations and variations in time series data, and predict the impact of COVID-19 on industrial systems. The minimum mean absolute percentage errors (MAPEs) of the electricity consumption, added value, and electricity usage efficiency of industrial enterprises separately are 0.12%, 0.10%, and 3.01% in the training stage, while those in the test stage are 6.79%, 4.09%, and 2.25%, respectively. The electricity consumption, added value, and electricity usage efficiency of industrial enterprises in Zhejiang Province will still present a tendency to grow with seasonal fluctuations from 2020 to 2022. Of them, the added value is predicted to increase the fastest, followed by electricity consumption. Elsevier Ltd. 2021-05-01 2021-01-24 /pmc/articles/PMC9758556/ /pubmed/36570723 http://dx.doi.org/10.1016/j.energy.2021.119952 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Chen, Hai-Bao Pei, Ling-Ling Zhao, Yu-Feng Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach |
title | Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach |
title_full | Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach |
title_fullStr | Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach |
title_full_unstemmed | Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach |
title_short | Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach |
title_sort | forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758556/ https://www.ncbi.nlm.nih.gov/pubmed/36570723 http://dx.doi.org/10.1016/j.energy.2021.119952 |
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