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Data-driven analytics for sustainabl...

Zhang, Xingxing.

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Data-driven analytics for sustainable buildings and cities = from theory to application /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven analytics for sustainable buildings and cities/ edited by Xingxing Zhang.
Reminder of title:	from theory to application /
other author:	Zhang, Xingxing.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	ix, 450 p. :ill., digital ;24 cm.
[NT 15003449]:	The 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.
Contained By:	Springer Nature eBook
Subject:	Sustainable buildings - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-16-2778-1
ISBN:	9789811627781

Data-driven analytics for sustainable buildings and cities = from theory to application /
Data-driven analytics for sustainable buildings and citiesfrom theory to application /[electronic resource] :edited by Xingxing Zhang. - Singapore :Springer Singapore :2021. - ix, 450 p. :ill., digital ;24 cm. - Sustainable development goals series,2523-3092. - Sustainable development goals series..

The 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.

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.

ISBN: 9789811627781

Standard No.: 10.1007/978-981-16-2778-1doiSubjects--Topical Terms:

3518070
Sustainable buildings
--Data processing.

LC Class. No.: TH880

Dewey Class. No.: 720.47

Data-driven analytics for sustainable buildings and cities = from theory to application /
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505 0 $a The 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.
520 $a 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.
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