Interpretable data-driven building energy analytics

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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.