Data-driven Analytics for Sustainable Buildings and Cities

1. Front Matter

   Pages i-ix

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2. The Evolving of Data-Driven Analytics for Buildings and Cities Towards Sustainability

   • Xingxing Zhang

   Pages 1-7

3. Energy in Buildings

   1. Front Matter

      Pages 9-9

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   2. Data-Driven Approaches for Prediction and Classification of Building Energy Consumption

      • Yixuan Wei, Xingxing Zhang, Yong Shi

      Pages 11-45

   3. Prediction of Occupancy Level and Energy Consumption in Office Building Using Blind System Identification and Neural Networks

      • Jinshun Wu, Yixuan Wei, Xingxing Zhang

      Pages 47-79

   4. Cluster Analysis for Occupant-Behaviour Based Electricity Load Patterns in Buildings: A Case Study in Shanghai Residences

      • Song Pan, Da Yan, Xingxing Zhang, Yixuan Wei

      Pages 81-92

   5. A Data-Driven Model Predictive Control for Lighting System Based on Historical Occupancy in an Office Building: Methodology Development

      • Yuan Jin, Da Yan, Xingxing Zhang, Jingjing An, Mengjie Han

      Pages 93-114

   6. Tailoring Future Climate Data for Building Energy Simulation

      • Jiale Chai, Pei Huang, Jingchun Shen, Xingxing Zhang

      Pages 115-139

   7. A Solar Photovoltaic/Thermal (PV/T) Concentrator for Building Application in Sweden Using Monte Carlo Method

      • Yaxiu Gu, Xingxing Zhang

      Pages 141-161

4. Thermal Comfort and Air Quality in Buildings

   1. Front Matter

      Pages 163-163

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   2. Influencing Factors for Occupants’ Window-Opening Behaviour in an Office Building Through Logistic Regression and Pearson Correlation Approaches

      • Song Pan, Xinru Wang, Xingxing Zhang, Li Chang, Yiqiao Liu

      Pages 165-178

   3. Reinforcement Learning Methodologies for Controlling Occupant Comfort in Buildings

      • Mengjie Han, Ross May, Xingxing Zhang

      Pages 179-205

   4. A Novel Reinforcement Learning Method for Improving Occupant Comfort via Window Opening and Closing

      • Ross May, Mengjie Han, Xingxing Zhang

      Pages 207-226

   5. Development of an Adaptation Table to Enhance the Accuracy of the Predicted Mean Vote Model

      • Yu Li, Yacine Rezgui, Annie Guerriero, Xingxing Zhang, Mengjie Han, Sylvain Kubicki et al.

      Pages 227-247

   6. A Prediction Accuracy Weighted Voting Ensemble Method for Thermal Sensation Evaluation

      • Yu Li, Yacine Rezgui, Sylvain Kubicki, Annie Guerriero, Xingxing Zhang

      Pages 249-267

   7. Analysis and Interpretation of the Particulate Matter Concentrations at the Subway Stations by General Linear Model (GLM) and Correlation Analysis

      • Xinru Wang, Song Pan, Xingxing Zhang, Li Chang, Yiqiao Liu

      Pages 269-282

5. Sustainability in Communities and Cities

   1. Front Matter

      Pages 283-283

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   2. Genetic Algorithm for Transforming a Residential Building Cluster into Electricity Prosumers

      • Pei Huang, Marco Lovati, Xingxing Zhang

      Pages 285-315

   3. Genetic Algorithm for a Coordinated Control to Improve Performance for a Building Cluster with Energy Storage, Electric Vehicles, and Energy Sharing

      • Pei Huang, Xingxing Zhang

      Pages 317-336

   4. Genetic Algorithm and Mont Carlo Method for Global Sensitivity Analysis of Key Parameters Identification of Net Zero Energy Buildings Towards Power Grid Interaction Optimization

      • Yongjun Sun, Yelin Zhang, Xingxing Zhang

      Pages 337-358

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. 

• Energy
• Thermal Comfort
• Occupant Behavior
• District Control
• Future Climate
• Neural Networks
• Genetic Algorithm
• Clustering
• Agent-based Modelling
• Reinforcement Learning

• Dalarna University, Falun, Sweden

  Xingxing Zhang

Xingxing Zhang is an Associate Professor in energy technology at Dalarna University, Sweden. He has multidisciplinary research experience, especially in energy systems, energy data analytics, adaption to future climate and urban building energy modelling for sustainable transition. He is leading the City Information Modelling (CIM) group at the university, which includes technical, economic, and environmental analyses by interdisciplinary research methods from building physics, energy engineering, informatics, machine learning and artificial intelligence. He is active in EU, UK and China research networks, by working in Swedish national projects, Sweden-China joint project, Nordic research project, EU H2020/FP7 projects, EU cost action and IEA tasks. He has won the second place of “EU-China Dragon-star Innovation Prize” in 2015. He serves as Editor Board Member of two journals and the regular reviewer for many international journals. He has an Accredited Professional Certificate of Leadership in Energy and Environmental Design (LEED AP) and he is UK Chartered Engineer (CEng), Member of Chartered Institution of Building Services Engineers (CIBSE) and CIB Commission Member of W098 Intelligent and Responsive Buildings.

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