Interpretable data-driven building energy analytics

在过去的二十年里，机器学习已经在建筑研究中得到了开发和测试，并得到了数据可用性的提高，强大而经济的计算资源以及先进算法的帮助，并已证明其在提高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能源和碳问题。这个博士项目的主要目标是为数据驱动的能源建模创建一个可解释的“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，可以克服传统基于仿真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，过程或服务在真实的时间和真实的世界条件下的表现。此外，可以设计可解释的数据驱动方法，将联合收割机的人类和机器智能结合在“人在回路中的方法”中，其基本目标是加速建筑存量向净零过渡。另一个主要目标是开发“数字孪生”方法，以便在从早期设计到运营的建筑物生命周期的各个阶段都能应用，而且这种方法是可扩展的，从整体建筑分析向下扩展到个别建筑技术，向上扩展到建筑群。

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.