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001439620 001__ 1439620
001439620 003__ OCoLC
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001439620 019__ $$a1268573479
001439620 020__ $$a9789811627781$$q(electronic bk.)
001439620 020__ $$a9811627789$$q(electronic bk.)
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001439620 0247_ $$a10.1007/978-981-16-2778-1$$2doi
001439620 035__ $$aSP(OCoLC)1268205603
001439620 040__ $$aYDX$$beng$$epn$$cYDX$$dGW5XE$$dOCLCO$$dDKU$$dEBLCP$$dOCLCF$$dUKAHL$$dOCLCQ$$dCOM$$dOCLCO$$dOCLCQ
001439620 049__ $$aISEA
001439620 050_4 $$aTH880
001439620 08204 $$a720/.47$$223
001439620 24500 $$aData-driven analytics for sustainable buildings and cities :$$bfrom theory to application /$$cXingxing Zhang, editor.
001439620 260__ $$aSingapore :$$bSpringer,$$c2021.
001439620 300__ $$a1 online resource
001439620 336__ $$atext$$btxt$$2rdacontent
001439620 337__ $$acomputer$$bc$$2rdamedia
001439620 338__ $$aonline resource$$bcr$$2rdacarrier
001439620 347__ $$atext file
001439620 347__ $$bPDF
001439620 4901_ $$aSustainable development goals series,$$x2523-3092
001439620 5050_ $$aThe evolving of data-driven analytics for buildings and cities towards sustainability -- Data-driven approaches for prediction and classification of building energy consumption -- Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks -- Cluster Analysis for Occupant-behaviour based Electricity Load Patterns in Buildings: A Case Study in Shanghai Residences -- A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development -- Tailoring future climate data for building energy simulation -- A solar photovoltaic/thermal (PV/T) concentrator for building application in Sweden using Monte Carlo method -- Influencing factors for occupants' window-opening behaviour in an office building through logistic regression and Pearson correlation approaches -- Reinforcement learning methodologies for controlling occupant comfort in buildings -- A novel Reinforcement learning method for improving occupant comfort via window opening and closing. 2942492291991671341156161.
001439620 506__ $$aAccess limited to authorized users.
001439620 520__ $$aThis book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behavior, thermal comfort, air quality and economic modelling across levels of building, communities and cities, through various data analytical approaches. It highlights the complex interplay of heating/cooling, ventilation and power systems in different processes, such as design, renovation and operation, for buildings, communities and cities. Methods from classical statistics, machine learning and artificial intelligence are applied into analyses for different building/urban components and systems. Knowledge from this book assists to accelerate sustainability of the society, which would contribute to a prospective improvement through data analysis in the liveability of both built and urban environment. This book targets a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it appeals to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.
001439620 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 21, 2021).
001439620 650_0 $$aSustainable buildings$$xData processing.
001439620 650_0 $$aSustainable buildings$$xStatistical methods.
001439620 650_0 $$aSmart cities.
001439620 650_6 $$aConstructions durables$$xInformatique.
001439620 650_6 $$aConstructions durables$$xMéthodes statistiques.
001439620 650_6 $$aVilles intelligentes.
001439620 655_0 $$aElectronic books.
001439620 7001_ $$aZhang, Xingxing,$$eeditor.
001439620 77608 $$iPrint version:$$tData-driven analytics for sustainable buildings and cities.$$dSingapore : Springer, 2021$$z9811627770$$z9789811627774$$w(OCoLC)1247677655
001439620 830_0 $$aSustainable development goals series,$$x2523-3092
001439620 852__ $$bebk
001439620 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-2778-1$$zOnline Access$$91397441.1
001439620 909CO $$ooai:library.usi.edu:1439620$$pGLOBAL_SET
001439620 980__ $$aBIB
001439620 980__ $$aEBOOK
001439620 982__ $$aEbook
001439620 983__ $$aOnline
001439620 994__ $$a92$$bISE