Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Contributions to Decision Support Systems, Energy Economics, and Shared Micromobility Research

This thesis includes research articles on Decision Support Systems, Energy Informatics, and Economics, Shared Micromobility, and Digital Study Assistance. For many years, established Information Systems (IS) scholars have called for solutionoriented research to address the most pressing problems of climate change. In this context, this thesis summarizes three consecutive research articles that present the multi-year development of a Decision Support System (DSS) for the energy transformation of the building sector. The DSS Nano Energy System Simulator (NESSI) was developed using Design Science Research guidelines and was further field tested and evaluated with stakeholders. In the discipline of Energy Informatics, a research article is presented that provides a morphological box for the classification of real microgrids. Next, a research article is presented that used regression analysis to investigate the influences of factors on residential photovoltaic system prices and revealed spatial price heterogeneity in Germany. Three research articles are outlined in the Shared Micromobility field. The first article uses a multi-year dataset of location data to examine the spatial and temporal use of e-scooters in Berlin. The second article builds on this and quantifies the influences of various factors such as weather, Covid-19 lockdowns, and other socio-economic parameters on the use of three micromobility concepts. The third article uses a web content mining process to collect a large dataset of police reports on e-scooter accidents. It analyzes risk factors as well as accident implications for riders. A research article on the requirements analysis and development of a digital study assistant concludes this thesis. Here, quantitative surveys and qualitative expert interviews are used to collect requirements from higher education institution stakeholders for a digital study assistant. In addition, developing a study assistance prototype is demonstrated and tested in the field

Towards a circular building industry through digitalisation

This thesis explores the integration of Circular Economy (CE) principles of narrow, slow, close, and regenerate in the social housing practice through digital technologies. Beginning with the examination of the CE implementation in Dutch social housing organisations, the research extends its focus to the broader built environment, introducing the Circular Digital Built Environment Framework and identifying ten enabling technologies. Subsequent chapters explore realworld applications of these digital technologies in circular new built, renovation, maintenance, and demolition projects of forerunner social housing organisations. The thesis includes a comprehensive study of material passports, addressing challenges around data management and proposing a digitally-enabled framework. The thesis concludes with critical reflections on the findings and their implications and provides further recommendations for research and practical applications in advancing circularity in the building industry through digital technologies

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity