Xingxing Zhang - Data-driven Analytics for Sustainable Buildings and Cities: From Theory to Application

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The integration of data-driven analytics can be a solution to the challenges of energy transition, sustainable economic growth and mitigated climate change in built environment and city context. Buildings, communities and cities can be more vibrant, efficient and resilient if they are analysed and optimized as a complex multi-physics system based on big data sets.

This book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behaviour, thermal comfort, air quality and economic/business 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 (e.g. regression/correlation, reinforcement learning, neural networks, genetic algorithm, clustering, agent-based modelling) are applied into the analyses for different building/urban components and systems. Knowledge from this book will assist 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 at a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it will appeal to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.