Publication:
Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings

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dc.citedby11
dc.contributor.authorNur-E-Alam M.en_US
dc.contributor.authorZehad Mostofa K.en_US
dc.contributor.authorKar Yap B.en_US
dc.contributor.authorKhairul Basher M.en_US
dc.contributor.authorAminul Islam M.en_US
dc.contributor.authorVasiliev M.en_US
dc.contributor.authorSoudagar M.E.M.en_US
dc.contributor.authorDas N.en_US
dc.contributor.authorSieh Kiong T.en_US
dc.contributor.authorid57197752581en_US
dc.contributor.authorid58880900300en_US
dc.contributor.authorid58881467500en_US
dc.contributor.authorid58880754700en_US
dc.contributor.authorid57828419400en_US
dc.contributor.authorid16053621100en_US
dc.contributor.authorid57194384501en_US
dc.contributor.authorid7201994841en_US
dc.contributor.authorid15128307800en_US
dc.date.accessioned2025-03-03T07:45:01Z
dc.date.available2025-03-03T07:45:01Z
dc.date.issued2024
dc.description.abstractThe focus of this work is on the optimization of an all-photovoltaic hybrid power generation systems for energy-efficient and sustainable buildings, aiming for net-zero emissions. This research proposes a hybrid approach combining conventional solar panels with advanced solar window systems and building integrated photovoltaic (BIPV) systems. By analyzing the meteorological data and using the simulation models, we predict energy outputs for different cities such as Kuala Lumpur, Sydney, Toronto, Auckland, Cape Town, Riyadh, and Kuwait City. Although there are long payback times, our simulations demonstrate that the proposed all-PV blended system can meet the energy needs of modern buildings (up to 78%, location dependent) and can be scaled up for entire buildings. The simulated results indicate that Middle Eastern cities are particularly suitable for these hybrid systems, generating approximately 1.2 times more power compared to Toronto, Canada. Additionally, we predict the outcome of the possible incorporation of intelligent and automated systems to boost overall energy efficiency toward achieving a sustainable building environment. ? 2024 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo103636
dc.identifier.doi10.1016/j.seta.2024.103636
dc.identifier.scopus2-s2.0-85184773743
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184773743&doi=10.1016%2fj.seta.2024.103636&partnerID=40&md5=7a409d73f817e1ab975890e79ad5aac9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36833
dc.identifier.volume62
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleSustainable Energy Technologies and Assessments
dc.subjectAutomation
dc.subjectCarbon
dc.subjectEnergy efficiency
dc.subjectIntelligent buildings
dc.subjectMachine learning
dc.subjectMeteorology
dc.subjectRenewable energy
dc.subjectSolar panels
dc.subjectSolar power generation
dc.subjectSustainable development
dc.subjectBlended systems
dc.subjectBuilding applications
dc.subjectHybrid energy system
dc.subjectLow-carbon emissions
dc.subjectMachine-learning
dc.subjectNet-zero building application
dc.subjectPhotovoltaics
dc.subjectSustainable building
dc.subjectSustainable energy
dc.subjectToronto
dc.subjectbuilding
dc.subjectcarbon emission
dc.subjectenergy efficiency
dc.subjectmachine learning
dc.subjectphotovoltaic system
dc.subjectsustainability
dc.subjectHybrid systems
dc.titleMachine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildingsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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