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

在能源系统快速脱碳背景下确定建筑改造路径的数据驱动方法

• 批准号: 2423643
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2423643
• 关键词: Data; driven; approach; identifying; building

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.

近年来，从现场传感器测量的大型时间序列数据集来表征建筑物的热性能方面取得了重大进展。结合热性能估计从这样的分析与建筑物的特性的细节将提供预测节能的机会，从具体的改造措施。这也将有助于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埃尔贝CDT社区接受培训，并加入一个积极支持的研究小组：建筑物的物理特性。该博士旨在开发一种新的方法，联合收割机结合属性的估计能源性能与补充物理数据，提供有关建筑性能的详细见解以及一年内的变化情况。这种建筑特性将使不同的加热技术和控制策略的影响进行评估。因此，博士将支持有效的决策，以实现灵活或智能的供暖操作，从而控制设备以支持平衡电网-这对脱碳至关重要。