Data driven approach to identifying building retrofit pathways in the context of rapid energy system decarbonisation

近年来，在利用现场传感器测量的大型时间序列数据集描述建筑物的热性能方面取得了重大进展。将此类分析得出的热性能评估与建筑特征的细节相结合，将为预测具体改造措施的节能提供机会。它还将能够对热泵等低碳技术的适用性进行强有力的评估，以向物业提供所需的热量。成功的候选人将在我们充满活力的ERBE CDT社区接受培训，并加入一个积极和支持性的研究小组：建筑物的物理特征。该博士旨在开发一种新方法，将估计的能源性能与补充的物理数据相结合，以提供对建筑性能的详细见解，以及它在一年内的变化情况。这种建筑特征将使不同的加热技术和控制策略的影响进行评估。因此，博士学位将支持有效的决策制定，以实现灵活或智能的供暖操作，从而控制家电以支持平衡电网——这对脱碳至关重要。

In recent years there have been significant advances in characterising the thermal performance of buildings from large time-series data sets of measurements taken by in situ sensors. Combining the thermal performance estimation from such analysis with details of the building's characteristics will provide the opportunity to predict energy savings from specific retrofit measures. It will also enable a robust assessment of the suitability of low carbon technologies, such as heat pumps, to deliver the required heat to properties.The successful candidate will be trained within our vibrant ERBE CDT community and join an active and supportive research group: The Physical Characterisation of Buildings.This PhD aims to develop a new method to combine the estimated energy performance of properties with supplementary physical data to provide detailed insights into building performance and how this varies over a year. This building characterisation will enable the impact of different heating technologies and control strategies to be assessed. The PhD will therefore support effective decision making to enable flexible, or smart, operation of heating, whereby appliances are controlled to support balancing the grid - essential for decarbonisation.