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.

在过去的二十年中，在建筑研究中开发和测试了机器学习，并获得了增加的数据可用性，功能强大且负担得起的计算资源以及高级算法，并证明了其提高建筑性能的潜力。所使用的方法将解决新建和改造中的运营和体现的能量和碳。该博士项目的主要目标是为数据驱动的能源建模创建一种可解释的“数字双胞胎”方法。 “ Digital Twin”是可以克服传统基于模拟工程方法的局限性的物理对象，过程或服务的数字复制品。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，过程或服务在实时和现实世界中的执行方式。此外，可以设计可解释的数据驱动方法来将人类和机器智能结合在“人类的方法”中，其基本目标是加速向建筑库存的净零净值过渡。另一个主要目标是开发“数字双胞胎”方法，以便可以在建筑物生命周期的各个阶段（从早期设计到操作）应用，并且它是可扩展的，从整体构建分析向下到单个建筑技术，再到建筑物的集群。