The ReBuild Initiative - A nexus for research into data-driven retrofit solutions for energy-efficient buildings

重建计划 - 研究数据驱动的节能建筑改造解决方案的纽带

• 批准号: 566285-2021
• 负责人: Evins, Ralph
• 金额: $ 10.5万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722262
• 关键词: ReBuild; Initiative; nexus; research; into

Reducing energy use in existing buildings is key to meeting climate change mitigation goals. Climate change disproportionally affects disadvantaged peoples, and retrofits can improve their housing conditions. Better computer models of how our buildings are performing are needed to give robust design solutions and evidence-based policies. Data-driven methods that use machine-learning have great potential as our buildings provide lots of data, but little is currently used for reducing emissions. These can be combined with existing models based on physics, and also used to speed up the simulation process.The ReBuild Initiative is an industry-government-academia consortium that will undertake 16 research activities that encompass the breadth and complexity of the challenge, each co-designed with a partner organization to address their needs. It is important to find synergies between the methods used by professionals, service providers and policy-makers. These collaborations will help refine and address key questions in data-driven building energy retrofits such as: Which machine learning methods are effective in tackling each problem? How can these methods integrate with existing simulation-based approaches? What datasets are needed to power them, and how can they be evaluated? How should these developments be leveraged and deployed to deliver improved tools, analyses and policy measures?The goals of the Initiative are: (a) to develop new methods and tools for building energy retrofit decisions using data-driven approaches, (b) to facilitate the exchange of knowledge, data and software between academia, industry and government partners, and (c) to influence future policy and regulation in Canada regarding existing buildings. Web-based tools will be tested and applied by partners to improve profitability, market penetration and efficiency. It will inform the development of a code for existing buildings and other policies at municipal, provincial and federal levels. It will also train a cohort of skilled workers with expertise to apply cutting-edge methods to pressing problems in industry, government and academia.

减少现有建筑物的能源使用是实现减缓气候变化目标的关键。气候变化对弱势群体造成不利影响，改造可以改善他们的住房条件。我们需要更好的建筑物性能计算机模型，以提供稳健的设计解决方案和基于证据的政策。使用机器学习的数据驱动方法具有巨大的潜力，因为我们的建筑物提供了大量数据，但目前很少用于减少排放。这些模型可以与现有的基于物理的模型相结合，也可以用于加速模拟过程。重建计划是一个由工业、政府和学术界组成的联盟，将开展16项研究活动，涵盖挑战的广度和复杂性，每项研究活动都与合作伙伴组织共同设计，以满足他们的需求。必须在专业人员、服务提供者和决策者所使用的方法之间找到协同增效作用。这些合作将有助于完善和解决数据驱动的建筑能源改造中的关键问题，例如：哪些机器学习方法可以有效地解决每个问题？这些方法如何与现有的基于模拟的方法相结合？需要哪些数据集来支持它们，以及如何评估它们？应如何利用和部署这些发展，以提供更好的工具、分析和政策措施？该倡议的目标是：（a）开发新的方法和工具，利用数据驱动的方法作出建筑物能源改造决定;（B）促进学术界、工业界和政府伙伴之间的知识、数据和软件交流;（c）影响加拿大今后有关现有建筑物的政策和法规。合作伙伴将测试和应用基于网络的工具，以提高盈利能力、市场渗透率和效率。它将为市、省和联邦各级现有建筑物法规和其他政策的制定提供信息。它还将培训一批具有专业知识的技术工人，以应用尖端方法解决工业，政府和学术界的紧迫问题。