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Titlebook: Data-driven Analytics for Sustainable Buildings and Cities; From Theory to Appli Xingxing Zhang Book 2021 The Editor(s) (if applicable) and

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书目名称Data-driven Analytics for Sustainable Buildings and Cities
副标题From Theory to Appli
编辑Xingxing Zhang
视频video
概述Explores the multidisciplinary fields of energy systems, occupant behavior, thermal comfort, and air quality.Applies a wide range of methods from classical statistics, machine learning, and artificial
丛书名称Sustainable Development Goals Series
图书封面Titlebook: Data-driven Analytics for Sustainable Buildings and Cities; From Theory to Appli Xingxing Zhang Book 2021 The Editor(s) (if applicable) and
描述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.. .
出版日期Book 2021
关键词Energy; Thermal Comfort; Occupant Behavior; District Control; Future Climate; Neural Networks; Genetic Alg
版次1
doihttps://doi.org/10.1007/978-981-16-2778-1
isbn_softcover978-981-16-2780-4
isbn_ebook978-981-16-2778-1Series ISSN 2523-3084 Series E-ISSN 2523-3092
issn_series 2523-3084
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Influencing Factors for Occupants’ Window-Opening Behaviour in an Office Building Through Logistic Rten regarded as window-opening behaviour, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants’ requirement for indoor thermal comfort but also influences building energy consumption. To learn more about potential factors having effects o
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A Novel Reinforcement Learning Method for Improving Occupant Comfort via Window Opening and Closingver, complex to predict and control conventionally. This chapter, therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and closing. The RL control aims at optimising the time point for window opening/closing through observing and learning from the
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