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Energy Consumption in a Smart City
Energy Consumption in a Smart City
Autore Nastasi Benedetto
Pubbl/distr/stampa Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica 1 electronic resource (270 p.)
Soggetto topico Research & information: general
Physics
Soggetto non controllato building energy flexibility
HOMER software
peak clipping
load shifting
energy saving
building performance assessment
indoor environment quality
occupants' satisfaction
post-occupancy evaluation
Green Building Index
tropical climate
building performance simulation
CO2 emission
occupant's comfort
window allocation
climate change
energy consumption
building energy load
thermal load
future weather
operative temperature
cooling load
daily energy need
solar gains
nZEB
historical buildings
TRNSYS
buildings retrofitting
buildings office
economic feasibility
Renewable Energy Systems (RESs)
Zero Energy District (ZED)
Digital Twin (DT)
Building Information Modelling (BIM)
Geographic Information System (GIS)
Revit software's
asymmetric duty cycle control
bifilar coil
pulse duty cycle control
induction heating
metal melting
phase shift control
pulse density modulation
series resonant inverter
variable frequency control
building operation and maintenance
extended reality
virtual reality
augmented reality
mixed reality
immersive technologies
digital twins
metaverse
positive energy district
district energy infrastructure
decarbonisation of neighbourhoods
GIS
energy transition
smart city policy
carbon emission intensity
digital transformation
green innovation
difference-in-differences
ISBN 3-0365-5963-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910637793403321
Nastasi Benedetto  
Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Innovative Learning Environments in STEM Higher Education [[electronic resource] ] : Opportunities, Challenges, and Looking Forward / / edited by Jungwoo Ryoo, Kurt Winkelmann
Innovative Learning Environments in STEM Higher Education [[electronic resource] ] : Opportunities, Challenges, and Looking Forward / / edited by Jungwoo Ryoo, Kurt Winkelmann
Autore Ryoo Jungwoo
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Springer Nature, 2021
Descrizione fisica 1 online resource (XV, 137 p. 8 illus., 7 illus. in color.)
Disciplina 519.5
Collana SpringerBriefs in Statistics
Soggetto topico Statistics 
Machine learning
Learning
Instruction
Knowledge representation (Information theory) 
Statistics for Social Sciences, Humanities, Law
Machine Learning
Statistics and Computing/Statistics Programs
Learning & Instruction
Knowledge based Systems
Educació STEM
Educació superior
Soggetto genere / forma Llibres electrònics
Soggetto non controllato Statistics for Social Sciences, Humanities, Law
Machine Learning
Statistics and Computing/Statistics Programs
Learning & Instruction
Knowledge based Systems
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Statistics and Computing
Education
Innovative Learning Environments
ILEs
Science, Technology, Engineering, and Math
STEM
virtual reality
VR
augmented reality
mixed reality
cross reality
extended reality
artificial intelligence
AI
adaptive learning
personalized learning
higher education
multimodal learning
mobile learning
Open Access
Social research & statistics
Mathematical & statistical software
Teaching skills & techniques
Cognition & cognitive psychology
Expert systems / knowledge-based systems
ISBN 3-030-58948-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. X-FILEs Vision for personalized and Adaptive Learning -- 3. X-FILEs Vision for Multi-modal Learning Formats -- 4. X-FILEs Vision for Extended/Cross Reality (XR) -- 5. X-FILEs Vision for Artificial Intelligence (AI) and Machine Learning (ML) -- 6. Cross-Cutting Concerns -- 7. Epilogue.
Record Nr. UNISA-996466564503316
Ryoo Jungwoo  
Springer Nature, 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Innovative Learning Environments in STEM Higher Education [[electronic resource] ] : Opportunities, Challenges, and Looking Forward / / edited by Jungwoo Ryoo, Kurt Winkelmann
Innovative Learning Environments in STEM Higher Education [[electronic resource] ] : Opportunities, Challenges, and Looking Forward / / edited by Jungwoo Ryoo, Kurt Winkelmann
Autore Ryoo Jungwoo
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Springer Nature, 2021
Descrizione fisica 1 online resource (XV, 137 p. 8 illus., 7 illus. in color.)
Disciplina 519.5
Collana SpringerBriefs in Statistics
Soggetto topico Statistics 
Machine learning
Learning
Instruction
Knowledge representation (Information theory) 
Statistics for Social Sciences, Humanities, Law
Machine Learning
Statistics and Computing/Statistics Programs
Learning & Instruction
Knowledge based Systems
Educació STEM
Educació superior
Soggetto genere / forma Llibres electrònics
Soggetto non controllato Statistics for Social Sciences, Humanities, Law
Machine Learning
Statistics and Computing/Statistics Programs
Learning & Instruction
Knowledge based Systems
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Statistics and Computing
Education
Innovative Learning Environments
ILEs
Science, Technology, Engineering, and Math
STEM
virtual reality
VR
augmented reality
mixed reality
cross reality
extended reality
artificial intelligence
AI
adaptive learning
personalized learning
higher education
multimodal learning
mobile learning
Open Access
Social research & statistics
Mathematical & statistical software
Teaching skills & techniques
Cognition & cognitive psychology
Expert systems / knowledge-based systems
ISBN 3-030-58948-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. X-FILEs Vision for personalized and Adaptive Learning -- 3. X-FILEs Vision for Multi-modal Learning Formats -- 4. X-FILEs Vision for Extended/Cross Reality (XR) -- 5. X-FILEs Vision for Artificial Intelligence (AI) and Machine Learning (ML) -- 6. Cross-Cutting Concerns -- 7. Epilogue.
Record Nr. UNINA-9910473457603321
Ryoo Jungwoo  
Springer Nature, 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui