Machine learning for a sustainable energy future

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dc.authorid0000-0001-5077-5689
dc.authorid0000-0003-1282-718X
dc.authorid0000-0003-0576-8724
dc.authorid0000-0002-1366-9702
dc.contributor.authorOral, Burcu
dc.contributor.authorCosgun, Ahmet
dc.contributor.authorKilic, Aysegul
dc.contributor.authorEroglu, Damla
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorYildirim, Ramazan
dc.date.accessioned2026-04-04T18:55:38Z
dc.date.available2026-04-04T18:55:38Z
dc.date.issued2025
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractEnergy production is one of the key enablers for human activities such as food and clean water production, transportation, telecommunication, education, and healthcare; however, it is also the main cause of global warming. Hence, sustainable energy is critical for most United Nations (UN) Sustainable Development Goals (SDGs), and it is directly targeted in SDG7. In this review, we analyze the potential role of machine learning (ML), another enabler technology, in sustainable energy and SGDs. We review the use of ML in energy production and storage as well as in energy forecasting and planning activities and provide our perspective on the challenges and opportunities for the future role of ML. Although there are strong challenges for both sustainable energy supply (like conflict between the urgent energy needs and global warming) and ML applications (like high energy consumption in ML applications and risk of increasing inequalities among people and nations), ML may make significant contributions to sustainable energy efforts and therefore to the achievement of SDGs through monitoring and remote sensing to collect data, planning the worldwide efforts and improving the performance of new and more sustainable energy technologies.
dc.identifier.doi10.1039/d4cc05148c
dc.identifier.doi10.1039/d4cc05148c
dc.identifier.endpage1370
dc.identifier.issn1359-7345
dc.identifier.issn1364-548X
dc.identifier.issue7
dc.identifier.pmid39704098
dc.identifier.scopus2-s2.0-85212934349
dc.identifier.scopusqualityQ1
dc.identifier.startpage1342
dc.identifier.urihttps://doi.org/10.1039/d4cc05148c
dc.identifier.urihttps://hdl.handle.net/11411/10500
dc.identifier.volume61
dc.identifier.wosWOS:001380281200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherRoyal Soc Chemistry
dc.relation.ispartofChemical Communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectNatural-Gas Consumption
dc.subjectNeural-Network
dc.subjectElectricity Demand
dc.subjectWavelet Transform
dc.subjectTime-Series
dc.subjectWind Energy
dc.subjectDatabase
dc.subjectPerformance
dc.subjectPrediction
dc.subjectDesign
dc.titleMachine learning for a sustainable energy future
dc.typeReview Article

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