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230202 ||| eng |
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|a books978-3-0365-5963-6
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|a 9783036559643
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|a 9783036559636
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1 |
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|a Nastasi, Benedetto
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| 245 |
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|a Energy Consumption in a Smart City
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (270 p.)
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| 653 |
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|a operative temperature
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| 653 |
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|a thermal load
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| 653 |
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|a digital twins
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| 653 |
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|a tropical climate
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| 653 |
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|a Geographic Information System (GIS)
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| 653 |
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|a post-occupancy evaluation
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| 653 |
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|a mixed reality
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| 653 |
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|a phase shift control
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| 653 |
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|a building performance assessment
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| 653 |
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|a pulse duty cycle control
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| 653 |
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|a n/a
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| 653 |
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|a induction heating
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| 653 |
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|a asymmetric duty cycle control
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| 653 |
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|a district energy infrastructure
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| 653 |
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|a energy consumption
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| 653 |
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|a digital transformation
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| 653 |
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|a Building Information Modelling (BIM)
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| 653 |
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|a immersive technologies
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| 653 |
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|a Zero Energy District (ZED)
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| 653 |
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|a TRNSYS
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| 653 |
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|a load shifting
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| 653 |
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|a buildings retrofitting
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| 653 |
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|a building operation and maintenance
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| 653 |
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|a HOMER software
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| 653 |
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|a occupants' satisfaction
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| 653 |
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|a GIS
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| 653 |
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|a extended reality
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| 653 |
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|a occupant's comfort
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| 653 |
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|a virtual reality
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| 653 |
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|a metal melting
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| 653 |
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|a cooling load
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| 653 |
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|a building energy load
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| 653 |
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|a energy transition
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| 653 |
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|a series resonant inverter
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| 653 |
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|a CO2 emission
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| 653 |
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|a Research & information: general / bicssc
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| 653 |
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|a carbon emission intensity
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| 653 |
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|a Physics / bicssc
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| 653 |
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|a variable frequency control
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| 653 |
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|a Green Building Index
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| 653 |
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|a smart city policy
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| 653 |
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|a Digital Twin (DT)
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| 653 |
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|a Revit software's
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| 653 |
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|a green innovation
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| 653 |
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|a energy saving
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| 653 |
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|a difference-in-differences
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| 653 |
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|a solar gains
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| 653 |
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|a Renewable Energy Systems (RESs)
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| 653 |
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|a historical buildings
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| 653 |
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|a indoor environment quality
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| 653 |
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|a decarbonisation of neighbourhoods
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|a daily energy need
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| 653 |
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|a pulse density modulation
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| 653 |
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|a building performance simulation
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| 653 |
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|a metaverse
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| 653 |
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|a climate change
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| 653 |
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|a window allocation
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| 653 |
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|a building energy flexibility
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| 653 |
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|a future weather
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| 653 |
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|a positive energy district
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| 653 |
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|a peak clipping
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| 653 |
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|a bifilar coil
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| 653 |
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|a buildings office
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| 653 |
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|a augmented reality
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| 653 |
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|a economic feasibility
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| 653 |
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|a nZEB
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| 700 |
1 |
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|a Mauri, Andrea
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| 700 |
1 |
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|a Nastasi, Benedetto
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| 700 |
1 |
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|a Mauri, Andrea
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.3390/books978-3-0365-5963-6
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/94568
|z DOAB: description of the publication
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/6403
|7 0
|x Verlag
|3 Volltext
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| 082 |
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|a 900
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|a 551.6
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|a 363
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|a 000
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|a 333
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|a 530
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|a 700
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|a 330
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|a A Smart City is the perfect environment to study and exploit the interactions between actors because its architecture already integrates vaious elements to collect data and connect to its citizens. Furthermore, the proliferation of web platforms (e.g., social media and web fora) and the increased affordability of sensors and IoT devices (e.g., smart meters) make data related to a large and diverse set of users accessible, as their activities in the digital world reflect their real-life actions. These new technologies can be of great use for the stakeholders as, on the one hand, they provide them with semantically rich inputs and frequent updates at a relatively cheap cost and, on the other, form a direct channel of communication with the citizens. To fully exploit these new data sources, we need both novel computational methods (e.g., AI, data mining algorithms, knowledge representation) that are suitable for analyzing and understanding the dynamics behind energy consumption and also a deeper understanding of how these methods can be integrated into the existing design and decision processes (e.g., human-in-the-loop processes).Therefore, this Special Issue welcomed original multidisciplinary research works about AI, data science methods, and their integration in existing design/decision-making processes in the domain of energy consumption in Smart Cities.
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