Autonomous Energy Management by Applying Deep Q-Learning to Enhance Sustainability in Smart Tourism Cities

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dc.contributor.authorPannee Suanpang
dc.contributor.authorPitchaya Jamjuntr
dc.contributor.authorKittisak Jermsittiparsert
dc.contributor.authorPhuripoj Kaewyong
dc.contributor.correspondenceP. Suanpang; Faculty of Science and Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th
dc.date.accessioned2025-03-10T07:35:06Z
dc.date.available2025-03-10T07:35:06Z
dc.date.issued2022
dc.description.abstractAutonomous energy management is becoming a significant mechanism for attaining sustainability in energy management. This resulted in this research paper, which aimed to apply deep reinforcement learning algorithms for an autonomous energy management system of a microgrid. This paper proposed a novel microgrid model that consisted of a combustion set of a household load, renewable energy, an energy storage system, and a generator, which were connected to the main grid. The proposed autonomous energy management system was designed to cooperate with the various flexible sources and loads by defining the priority resources, loads, and electricity prices. The system was implemented by using deep reinforcement learning algorithms that worked effectively in order to control the power storage, solar panels, generator, and main grid. The system model could achieve the optimal performance with near-optimal policies. As a result, this method could save 13.19% in the cost compared to conducting manual control of energy management. In this study, there was a focus on applying Q-learning for the microgrid in the tourism industry in remote areas which can produce and store energy. Therefore, we proposed an autonomous energy management system for effective energy management. In future work, the system could be improved by applying deep learning to use energy price data to predict the future energy price, when the system could produce more energy than the demand and store it for selling at the most appropriate price; this would make the autonomous energy management system smarter and provide better benefits for the tourism industry. This proposed autonomous energy management could be applied to other industries, for example businesses or factories which need effective energy management to maintain microgrid stability and also save energy. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.identifier.citationEnergies
dc.identifier.doi10.3390/en15051906
dc.identifier.issn19961073
dc.identifier.scopus2-s2.0-85126269975
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4601
dc.languageEnglish
dc.publisherMDPI
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectAutonomous energy
dc.subjectDeep Q-learning
dc.subjectEnergy management
dc.subjectSmart city
dc.subjectSmart tourism
dc.subjectSustainability
dc.titleAutonomous Energy Management by Applying Deep Q-Learning to Enhance Sustainability in Smart Tourism Cities
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85126269975&doi=10.3390%2fen15051906&partnerID=40&md5=32ff4b28004a83611033df402136ccb1
oaire.citation.issue5
oaire.citation.volume15
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