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Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

dc.contributor.editorDeschrijver, Dirk
dc.date.accessioned2022-01-11T13:29:24Z
dc.date.available2022-01-11T13:29:24Z
dc.date.issued2021
dc.identifierONIX_20220111_9783036512075_81
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/76345
dc.description.abstractIn October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.otherpassive house
dc.subject.otherenclosure structure
dc.subject.otherheat transfer coefficient
dc.subject.otherenergy consumption
dc.subject.otherturbo-propeller
dc.subject.otherregional
dc.subject.otherfuel
dc.subject.otherweight
dc.subject.otherrange
dc.subject.otherdesign
dc.subject.otherCO2 reduction
dc.subject.othermulti-objective combinatorial optimization
dc.subject.othermeta-heuristics
dc.subject.otherant colony optimization
dc.subject.othernon-intrusive load monitoring
dc.subject.otherappliance classification
dc.subject.otherappliance feature
dc.subject.otherrecurrence graph
dc.subject.otherweighted recurrence graph
dc.subject.otherV–I trajectory
dc.subject.otherconvolutional neural network
dc.subject.otherenergy baselines
dc.subject.othermachine learning
dc.subject.otherclustering
dc.subject.otherneural methods
dc.subject.othersmart intelligent systems
dc.subject.otherbuilding energy consumption
dc.subject.otherbuilding load forecasting
dc.subject.otherenergy efficiency
dc.subject.otherthermal improved of buildings
dc.subject.otheranti-icing
dc.subject.otherheat and mass transfer
dc.subject.otherheating power distribution
dc.subject.otherheat load reduction
dc.subject.otheroptimization method
dc.subject.otherexperimental validation
dc.subject.otherbig data process
dc.subject.otherpredictive maintenance
dc.subject.otherfracturing roofs to maintain entry (FRME)
dc.subject.otherfield measurement
dc.subject.othernumerical simulation
dc.subject.otherside abutment pressure
dc.subject.otherstrata movement
dc.subject.otherenergy
dc.subject.othermanufacturing
dc.subject.otherprediction
dc.subject.otherforecasting
dc.subject.othermodelling
dc.subject.othern/a
dc.titleImproving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-1206-8
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036512075
oapen.relation.isbn9783036512068
oapen.pages201
oapen.place.publicationBasel, Switzerland


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