Journal
ENERGY
Volume 222, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.119952
Keywords
Electricity consumption; Seasonal fluctuation; COVID-19 impact; Grey prediction
Categories
Funding
- National Natural Science Foundation of China [71801190, 71571157]
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This research aims to forecast seasonal fluctuations in electricity consumption and usage efficiency of industrial sectors, while identifying the impacts of COVID-19. A new model, AWBO-DGGM(1,1), is proposed to analyze the data from 2013 to 2020 in Zhejiang Province, China. The results show that the model can accurately predict seasonal variations and the impact of COVID-19 on industrial systems.
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. (c) 2021 Elsevier Ltd. All rights reserved.
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