@article{1439620,
      recid = {1439620},
      author = {Zhang, Xingxing,},
      title = {Data-driven analytics for sustainable buildings and cities  : from theory to application /},
      publisher = {Springer,},
      address = {Singapore :},
      pages = {1 online resource},
      year = {2021},
      abstract = {This 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.},
      url = {http://library.usi.edu/record/1439620},
      doi = {https://doi.org/10.1007/978-981-16-2778-1},
}