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Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

dc.contributor.authorLytras, Miltiadis*
dc.contributor.authorChui, Kwok Tai*
dc.date.accessioned2021-02-11T08:31:15Z
dc.date.available2021-02-11T08:31:15Z
dc.date.issued2020*
dc.date.submitted2020-06-09 16:38:57*
dc.identifier46125*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/41352
dc.description.abstractEnergy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.*
dc.languageEnglish*
dc.subjectTA1-2040*
dc.subjectT1-995*
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technologyen_US
dc.subject.otherartificial neural network*
dc.subject.otherhome energy management systems*
dc.subject.otherconditional random fields*
dc.subject.otherLR*
dc.subject.otherELR*
dc.subject.otherenergy disaggregation*
dc.subject.otherartificial intelligence*
dc.subject.othergenetic algorithm*
dc.subject.otherdecision tree*
dc.subject.otherstatic young’s modulus*
dc.subject.otherprice*
dc.subject.otherscheduling*
dc.subject.otherself-adaptive differential evolution algorithm*
dc.subject.otherMarsh funnel*
dc.subject.otherenergy*
dc.subject.otheryield point*
dc.subject.othernon-intrusive load monitoring*
dc.subject.othermud rheology*
dc.subject.otherdistributed genetic algorithm*
dc.subject.otherMCP39F511*
dc.subject.otherJetson TX2*
dc.subject.othersustainable development*
dc.subject.otherartificial neural networks*
dc.subject.othertransient signature*
dc.subject.otherload disaggregation*
dc.subject.othersmart villages*
dc.subject.otherambient assisted living*
dc.subject.othersmart cities*
dc.subject.otherdemand side management*
dc.subject.othersmart city*
dc.subject.otherCNN*
dc.subject.otherwireless sensor networks*
dc.subject.otherobject detection*
dc.subject.otherdrill-in fluid*
dc.subject.otherERELM*
dc.subject.othersandstone reservoirs*
dc.subject.otherRPN*
dc.subject.otherdeep learning*
dc.subject.otherRELM*
dc.subject.othersmart grids*
dc.subject.othermultiple kernel learning*
dc.subject.otherload*
dc.subject.otherfeature extraction*
dc.subject.otherNILM*
dc.subject.otherenergy management*
dc.subject.otherenergy efficient coverage*
dc.subject.otherinsulator*
dc.subject.otherFaster R-CNN*
dc.subject.otherhome energy management*
dc.subject.othersmart grid*
dc.subject.otherLSTM*
dc.subject.othersmart metering*
dc.subject.otheroptimization algorithms*
dc.subject.otherforecasting*
dc.subject.otherplastic viscosity*
dc.subject.othermachine learning*
dc.subject.othercomputational intelligence*
dc.subject.otherpolicy making*
dc.subject.othersupport vector machine*
dc.subject.otherinternet of things*
dc.subject.othersensor network*
dc.subject.othernonintrusive load monitoring*
dc.subject.otherdemand response*
dc.titleArtificial Intelligence for Smart and Sustainable Energy Systems and Applications*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03928-890-8*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
oapen.relation.isbn9783039288892*
oapen.relation.isbn9783039288908*
oapen.pages258*
oapen.edition1st*


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