Data driven approach to modelling the resource footprints of live data streams in digital twins

对数字孪生中实时数据流的资源足迹进行建模的数据驱动方法

• 批准号: 2560286
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2560286
• 关键词: Data; driven; approach; modelling; resource

Internet of Things technologies can improve the operational efficiency of buildings, creating "digital twins". This project assesses the whole lifecycle cost of data capture, analysis and storage to deliver sustainable digital twins for decarbonisation and demand management. Intelligent Buildings have been in operation for decades however it is only in recent years that data from these systems is starting to surface beyond building management systems. The ubiquity of the Internet of Things is changing how we sense and interact with our environment and has led to the emergence of the Digital Twin - a virtual model of a real-life operational entity. Driven by information, these models allow us to monitor operations to head off issues before they arise, or optimise operations based on ever changing human-environment interaction. This research will explore the hidden value of "data as a material" in 2 new campus buildings to improve efficiency and demand flexibility. At a global level, urgent action is required to decrease the carbon intensity of our buildings and to support a transition to a net zero carbon operation. As the potential volume of data grows exponentially it is essential that we strategically look at the whole life cost of this information to ensure the digital footprint cost is optimised compared to the benefit accrued. This PhD aims to develop a new method to quantify the social, economic and environmental benefits of capturing, analysing and storing information generated in a digital twin based on analysis of demand management and post occupancy evaluation. Analysis will be conducted on the digital footprint of two new campus buildings at UCL East (opening 2022 and 2023) which have the capacity to generate about 30 million data points per day. The research will explore the buildings in the context of operational factors such as improving facilities and the role of a Living Lab environment supporting research and teaching.

物联网技术可以提高建筑物的运营效率，创造出一对数字孪生兄弟。该项目评估数据捕获、分析和存储的整个生命周期成本，以交付用于脱碳和需求管理的可持续数字双胞胎。智能建筑已经运行了几十年，然而，直到最近几年，这些系统的数据才开始浮出建筑物管理系统之外。物联网的无处不在正在改变我们感知环境和与环境互动的方式，并导致了数字孪生的出现--一个真实运营实体的虚拟模型。在信息的驱动下，这些模型使我们能够监控运营，以便在问题出现之前将其阻止，或者根据不断变化的人与环境交互来优化运营。本研究将在2栋新校区建筑中挖掘数据作为材料的隐藏价值，以提高效率和需求灵活性。在全球层面上，需要采取紧急行动来降低我们建筑物的碳排放强度，并支持向净零碳运营过渡。随着潜在数据量呈指数级增长，我们必须战略性地审视这些信息的整个生命周期成本，以确保数字足迹成本相对于所获得的收益得到优化。本博士学位旨在开发一种新的方法，在需求管理和使用后评估的基础上，量化捕获、分析和存储在数字孪生兄弟中产生的信息所产生的社会、经济和环境效益。将对伦敦大学学院东校区两座新校区建筑(2022年和2023年开业)的数字足迹进行分析，这两座建筑每天可产生约3000万个数据点。这项研究将探索建筑物在操作因素的背景下，如改善设施和生活实验室环境对研究和教学的支持作用。