Interpretable data-driven building energy analytics

可解释的数据驱动的建筑能源分析

• 批准号: 2891094
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2891094
• 关键词: Interpretable; data; driven; building; energy

Over the last two decades, machine learning has been developed and tested in building research, aided by increased data availability, powerful and affordable computing resources, and advanced algorithms, and has demonstrated its potential to improve building performance. The methods used will address both operational and embodied energy and carbon in new construction and retrofit. The primary goal of this PhD project will be to create an interpretable "digital twin" approach for data-driven energy modelling. A "digital twin" is a digital replica of a physical object, process, or service that can overcome the limitations of traditional simulation-based engineering approaches. While simulations and digital twins are both virtual representations of objects, digital twins can verify how a physical object, process or service performs in real time and in real world conditions. Furthermore, interpretable data-driven methods can be designed to combine human and machine intelligence in a "human-in-the-loop approach" whose fundamental goal is to accelerate the transition to Net Zero of the building stock. The other primary goal will be to develop the "digital twin" approach so that it can be applied at various stages of the building life cycle, from early design to operation, and that it is scalable, from whole-building analysis downwards to individual building technologies and upward to clusters of buildings.

在过去的二十年里，机器学习在建筑研究中得到了发展和测试，这得益于增加的数据可用性、强大和负担得起的计算资源以及先进的算法，并证明了它在改善建筑性能方面的潜力。所使用的方法将解决新建筑和改造中的运营和体现的能源和碳问题。这个博士项目的主要目标将是为数据驱动的能源建模创造一种可解释的“数字孪生”方法。“数字孪生”是物理对象、过程或服务的数字复制品，可以克服传统的基于模拟的工程方法的局限性。虽然模拟和数字双胞胎都是对象的虚拟表示，但数字双胞胎可以验证物理对象、流程或服务在实时和现实世界条件下的表现。此外，可以设计可解释的数据驱动的方法，将人的智能和机器的智能结合在一起，形成“人在回路中的方法”，其根本目标是加速向建筑库存净零的过渡。另一个主要目标是开发“数字孪生”方法，使其可应用于建筑生命周期的各个阶段，从早期设计到运行，并具有可伸缩性，从向下到单个建筑技术，再到建筑群。