An Intelligent Recommendation for Intelligently Accessible Charging Stations: Electronic Vehicle Charging to Support a Sustainable Smart Tourism City

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dc.contributor.authorPannee Suanpang
dc.contributor.authorPitchaya Jamjuntr
dc.contributor.authorPhuripoj Kaewyong
dc.contributor.authorChawalin Niamsorn
dc.contributor.authorKittisak Jermsittiparsert
dc.contributor.correspondenceP. Suanpang; Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th; K. Jermsittiparsert; Faculty of Education, University of City Island, Famagusta, 9945, Cyprus; email: kittisak.jermsittiparsert@adakent.edu.tr
dc.date.accessioned2025-03-10T07:34:45Z
dc.date.available2025-03-10T07:34:45Z
dc.date.issued2023
dc.description.abstractThe world is entering an era of awareness of the preservation of natural energy sustainability. Therefore, electric vehicles (EVs) have become a popular alternative in todayÕs transportation system as they have zero emissions, save energy, and reduce pollution. One of the most significant problems with EVs is an inadequate charging infrastructure and spatially and temporally uneven charging demands. As such, EV drivers in many large cities frequently struggle to find suitable charging locations. Furthermore, the recent emergence of deep reinforcement learning has shown great promise for improving the charging experience in a variety of ways over the long term. In this paper, a Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) (Master) framework is proposed for intelligently public-accessible charging stations, taking into account several long-term spatio-temporal parameters. When compared to a random selection recommendation system, the experimental results demonstrate that an STMARL (master) framework has a long-term goal of lowering the overall charging wait time (CWT), average charging price (CP), and charging failure rate (CFR) of EVs. © 2022 by the authors.
dc.identifier.citationSustainability (Switzerland)
dc.identifier.doi10.3390/su15010455
dc.identifier.issn20711050
dc.identifier.scopus2-s2.0-85146002255
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4584
dc.languageEnglish
dc.publisherMDPI
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectdestination
dc.subjectelectric vehicle
dc.subjectelectronic vehicle charging
dc.subjectintelligent recommendation system
dc.subjectsmart city
dc.subjectsmart tourism
dc.titleAn Intelligent Recommendation for Intelligently Accessible Charging Stations: Electronic Vehicle Charging to Support a Sustainable Smart Tourism City
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85146002255&doi=10.3390%2fsu15010455&partnerID=40&md5=7af3eece2d9da4854c49f11d0f055d61
oaire.citation.issue1
oaire.citation.volume15
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