04866nam 2201153z- 450 9910637793403321202212063-0365-5963-9(CKB)5470000001631602(oapen)https://directory.doabooks.org/handle/20.500.12854/94568(oapen)doab94568(EXLCZ)99547000000163160220202212d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEnergy Consumption in a Smart CityBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (270 p.)3-0365-5964-7 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.PhysicsbicsscResearch & information: generalbicsscasymmetric duty cycle controlaugmented realitybifilar coilbuilding energy flexibilitybuilding energy loadBuilding Information Modelling (BIM)building operation and maintenancebuilding performance assessmentbuilding performance simulationbuildings officebuildings retrofittingcarbon emission intensityclimate changeCO2 emissioncooling loaddaily energy needdecarbonisation of neighbourhoodsdifference-in-differencesdigital transformationDigital Twin (DT)digital twinsdistrict energy infrastructureeconomic feasibilityenergy consumptionenergy savingenergy transitionextended realityfuture weatherGeographic Information System (GIS)GISGreen Building Indexgreen innovationhistorical buildingsHOMER softwareimmersive technologiesindoor environment qualityinduction heatingload shiftingmetal meltingmetaversemixed realityn/anZEBoccupant's comfortoccupants' satisfactionoperative temperaturepeak clippingphase shift controlpositive energy districtpost-occupancy evaluationpulse density modulationpulse duty cycle controlRenewable Energy Systems (RESs)Revit software'sseries resonant invertersmart city policysolar gainsthermal loadTRNSYStropical climatevariable frequency controlvirtual realitywindow allocationZero Energy District (ZED)PhysicsResearch & information: generalNastasi Benedettoedt1302975Mauri AndreaedtNastasi BenedettoothMauri AndreaothBOOK9910637793403321Energy Consumption in a Smart City3035414UNINA