LEADER 04866nam 2201153z- 450 001 9910637793403321 005 20221206 010 $a3-0365-5963-9 035 $a(CKB)5470000001631602 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94568 035 $a(oapen)doab94568 035 $a(EXLCZ)995470000001631602 100 $a20202212d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aEnergy Consumption in a Smart City 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (270 p.) 311 08$a3-0365-5964-7 330 $aA 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. 606 $aPhysics$2bicssc 606 $aResearch & information: general$2bicssc 610 $aasymmetric duty cycle control 610 $aaugmented reality 610 $abifilar coil 610 $abuilding energy flexibility 610 $abuilding energy load 610 $aBuilding Information Modelling (BIM) 610 $abuilding operation and maintenance 610 $abuilding performance assessment 610 $abuilding performance simulation 610 $abuildings office 610 $abuildings retrofitting 610 $acarbon emission intensity 610 $aclimate change 610 $aCO2 emission 610 $acooling load 610 $adaily energy need 610 $adecarbonisation of neighbourhoods 610 $adifference-in-differences 610 $adigital transformation 610 $aDigital Twin (DT) 610 $adigital twins 610 $adistrict energy infrastructure 610 $aeconomic feasibility 610 $aenergy consumption 610 $aenergy saving 610 $aenergy transition 610 $aextended reality 610 $afuture weather 610 $aGeographic Information System (GIS) 610 $aGIS 610 $aGreen Building Index 610 $agreen innovation 610 $ahistorical buildings 610 $aHOMER software 610 $aimmersive technologies 610 $aindoor environment quality 610 $ainduction heating 610 $aload shifting 610 $ametal melting 610 $ametaverse 610 $amixed reality 610 $an/a 610 $anZEB 610 $aoccupant's comfort 610 $aoccupants' satisfaction 610 $aoperative temperature 610 $apeak clipping 610 $aphase shift control 610 $apositive energy district 610 $apost-occupancy evaluation 610 $apulse density modulation 610 $apulse duty cycle control 610 $aRenewable Energy Systems (RESs) 610 $aRevit software's 610 $aseries resonant inverter 610 $asmart city policy 610 $asolar gains 610 $athermal load 610 $aTRNSYS 610 $atropical climate 610 $avariable frequency control 610 $avirtual reality 610 $awindow allocation 610 $aZero Energy District (ZED) 615 7$aPhysics 615 7$aResearch & information: general 700 $aNastasi$b Benedetto$4edt$01302975 702 $aMauri$b Andrea$4edt 702 $aNastasi$b Benedetto$4oth 702 $aMauri$b Andrea$4oth 906 $aBOOK 912 $a9910637793403321 996 $aEnergy Consumption in a Smart City$93035414 997 $aUNINA