Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction



Masrur Ahmed, Abul Abrar ORCID: 0000-0002-7941-3902, Bailek, Nadjem ORCID: 0000-0001-9051-8548, Abualigah, Laith ORCID: 0000-0002-2203-4549, Bouchouicha, Kada, Kuriqi, Alban ORCID: 0000-0001-7464-8377, Sharifi, Alireza ORCID: 0000-0001-7110-7516, Sareh, Pooya ORCID: 0000-0003-1836-2598, Al khatib, Abdullah Mohammad Ghazi ORCID: 0000-0002-1352-2348, Mishra, Pradeep ORCID: 0000-0003-4430-886X, Colak, Ilhami
et al (show 1 more authors) (2023) Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction. Energy Reports, 10. pp. 2152-2165.

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Abstract

Global energy consumption has increased significantly in recent decades due to changes in the industrial and economic sectors. Accurate demand estimates are critical for decision-makers to save operation and maintenance costs, improve energy reliability, and make informed decisions for future development. This study evaluates a newly proposed soft technique called Variational Mode Decomposition (VMD) to improve the accuracy of power consumption forecasts. To validate the experimental results, we compared the predicted energy consumption values with measured values from five geographically diverse countries, including developed and developing countries. The study examined different time horizons and performed seasonal evaluations. The VMD-BiGRU and VMD-LSTM models show consistent and superior prediction accuracy, outperforming other models by 20% to 50% on all evaluation measures. In addition, we evaluated the efficiency of VMD-based models over different forecast horizons and find that they are most effective for short- to medium-term forecasts (1 to 12 months). For longer-term forecasts, we recommend combining VMD with specialized techniques. Overall, this study recommends using VMD to forecast electricity consumption in different regions, emphasizing carefully considering forecast horizons for optimal results.

Item Type: Article
Divisions: Faculty of Science and Engineering > School of Engineering
Depositing User: Symplectic Admin
Date Deposited: 16 Oct 2023 14:46
Last Modified: 13 Nov 2023 15:46
DOI: 10.1016/j.egyr.2023.08.076
Open Access URL: https://doi.org/10.1016/j.egyr.2023.08.076
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3173770