Simulating and optimising the performance of the building stock.

模拟和优化建筑群的性能。

• 批准号: 2241033
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2241033
• 关键词: Simulating; optimising; performance; building; stock.

The UK building stock accounted for 34% of the nation's greenhouse gas emissions in 2014, and has a low replacement rate of around 1-1.5% per year. The importance thus falls on improving the energy efficiency of the existing stock to meet future national emission targets. Retrofitting is fundamental to improving the efficiency of existing buildings, however the associated decision-making process is subject to many conflicting criteria. This problem is exacerbated at the stock level where the decision-maker must balance factors including those which are energy-related, environmental, social, financial, and so forth. Furthermore, there is a lack of developed methods to assist with this. Retrofit optimisation tools have been developed and applied to individual buildings; however there are very few studies which perform stock-level optimisation. This research thus questions the extent to which it is possible to optimise building retrofits at the stock-level. The research aims to develop a methodology which allows multi-objective optimisation to be performed over a segment of the stock. This kind of approach is well suited to scenarios where there is a constraint on budget/resources, which is often the case in real-world scenarios. Such a methodology could be valuable to local authorities, many of which are currently in the process of determining the best strategies to decarbonise their building stock

2014年，英国建筑存量占全国温室气体排放量的34%，每年的替代率约为1-1.5%。因此，重要的是福尔斯提高现有库存的能源效率，以满足未来的国家排放目标。改造是提高现有建筑物效率的基础，但相关的决策过程受到许多相互冲突的标准的影响。这个问题在库存水平上更加严重，决策者必须平衡各种因素，包括与能源有关的因素、环境因素、社会因素、财务因素等等。此外，还缺乏成熟的方法来帮助解决这一问题。改造优化工具已经开发并应用于个别建筑物，但是很少有研究进行库存水平的优化。因此，这项研究质疑在库存水平上优化建筑改造的可能性。该研究旨在开发一种方法，允许在股票的一个部分进行多目标优化。这种方法非常适合于预算/资源受到限制的场景，这在现实世界的场景中经常发生。这种方法对地方政府可能很有价值，其中许多地方政府目前正在确定建筑物脱碳的最佳战略