A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior

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dc.authoridyaghoubi, elnaz/0000-0002-8672-4178
dc.authoridYaghoubi, Elaheh/0000-0003-2334-7819
dc.authoridKhamees, Ahmed Salih/0000-0003-3466-0800
dc.contributor.authorYaghoubi, Elaheh
dc.contributor.authorYaghoubi, Elnaz
dc.contributor.authorKhamees, Ahmed
dc.contributor.authorRazmi, Darioush
dc.contributor.authorLu, Tianguang
dc.date.accessioned2024-09-29T15:55:21Z
dc.date.available2024-09-29T15:55:21Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMachine learning (ML) and deep learning (DL) have enabled algorithms to autonomously acquire knowledge from data, facilitating predictive and decision-making capabilities without explicit programming. This transformative potential has reshaped industries by utilizing data-driven insights. ML and DL models have found extensive application within the domain of electric vehicle (EV) charging predictions. These techniques effectively forecast EV charging behavior, considering variables such as charging station location, time of day, battery state of charge, EV owner behavioral patterns, and weather conditions. This study aims to comprehensively evaluate ML and DL applications in forecasting EV charging behavior while introducing a systematic categorization, a notable gap in current literature. A comprehensive dataset, selected from both the Web of Science and the Scopus database, sourced from Elsevier Journal, was thoughtfully chosen to cover relevant research studies for the purpose of achieving this goal. Furthermore, our research emphasizes the significance of model evaluation and explores the usefulness of commonly employed ML and DL techniques within forecasting approaches, including Short-Term Load Forecasting (STLF), Medium-Term Load Forecasting (MTLF), and Long-Term Load Forecasting (LTLF) to ensure precise predictions. Within this framework, the selected publications are classified based on methodology, research focus, objectives, publication year, geographic origin, and research outcomes. While both ML and DL techniques exhibit substantial potential in predicting EV charging behavior and mitigating challenges posed by the rising adoption of EVs, our analysis demonstrates that ensemble learning techniques surpass them in terms of predictive performance.en_US
dc.description.sponsorshipShandong Excellent Young Scientists Fund Program (Overseas) [2022HWYQ-039]; State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources [LAPS23012]en_US
dc.description.sponsorshipAcknowledgements This study is supported by the Shandong Excellent Young Scientists Fund Program (Overseas) (Grant No. 2022HWYQ-039) and the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (Grant No. LAPS23012) .en_US
dc.identifier.doi10.1016/j.engappai.2024.108789
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85197459884en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2024.108789
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4608
dc.identifier.volume135en_US
dc.identifier.wosWOS:001265989900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectric vehicleen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectEnsemble learningen_US
dc.titleA systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavioren_US
dc.typeReviewen_US

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