Using surrogate models in the integrated design process for high-performance buildings

在高性能建筑的集成设计过程中使用替代模型

• 批准号: 543534-2019
• 负责人: Evins, Ralph
• 金额: $ 1.75万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680066
• 关键词: Using; surrogate; models; integrated; design

In December 2016, the Pan-Canadian Framework on Clean Growth and Climate Change committed to meeting its international commitment of a 30% reduction of greenhouse gas (GHG) emissions below 2005 levels by 2030, of which 20 million tonnes are expected to come from the buildings sector.The technology and construction processes to make low carbon buildings are well understood; however, the buildings industry inherently requires front-loaded decision-making with long-term implications on building stock. The buildings constructed now are expected to still be present in 2030 and even 2050. Conventional analysis of building energy performance, carbon accounting and life-cycle costs through detailed simulation is time-consuming and expensive. In order to achieve the GHG reduction targets, tools must be developed that integrate effectively into the early concept screening and design decision-making processes, allowing rapid, reliable evaluation of options and measures.This project will adapt a machine learning method termed surrogate modeling to the building simulation domain, with an emphasis on breaking up the larger problem into sub-models (such as separating enclosure loads from HVAC delivery) that better align with a multi-discipline, integrated design process, as well as incorporating industry knowledge and producing tools for practical use by engineers, architects and planners during early concept design phases.Surrogate modeling relies on having a large number of validated baseline models to efficiently explore the design space for a new design (applying machine learning optimization techniques to identify where the new design fits among a constellation of pre-rendered models, without having to create a new high-fidelity model). The quality of the underlying baseline models is therefore vital, and this project will develop a validation methodology partly based on industry workflows for quality control. This will in turn support future work generating high-quality baselines for academic research, and in finding effective application in industry.This project will finally take the modules and methods described above and develop interactive tools for use by industry professionals. This will include research into effective visualizations and interface design, and will incorporate an iterative feedback process through phases of needs assessment surveys, workshops and field testing throughout the project timeline.

2016年12月，泛加拿大清洁增长和气候变化框架承诺，到2030年将温室气体（GHG）排放量在2005年的基础上减少30%，其中2000万吨预计将来自建筑行业。人们对低碳建筑的技术和施工过程都很了解；然而，建筑行业天生就需要对建筑存量有长期影响的前期决策。现在建造的建筑预计在2030年甚至2050年仍然存在。传统的建筑能源性能分析、碳核算和生命周期成本的详细模拟既耗时又昂贵。为了实现温室气体减排目标，必须开发工具，有效地整合到早期概念筛选和设计决策过程中，以便对各种选择和措施进行快速、可靠的评估。该项目将采用一种被称为替代建模的机器学习方法，用于建筑仿真领域，重点是将更大的问题分解为子模型（例如将外壳负载从暖通空调交付中分离出来），从而更好地与多学科、集成的设计过程保持一致，并在早期概念设计阶段将行业知识和生产工具结合起来，供工程师、建筑师和规划师实际使用。代理建模依赖于拥有大量经过验证的基线模型来有效地探索新设计的设计空间（应用机器学习优化技术来识别新设计在预渲染模型群中的适合位置，而无需创建新的高保真模型）。因此，底层基线模型的质量是至关重要的，并且该项目将开发一种部分基于质量控制的行业工作流程的验证方法。这将反过来支持未来的工作，为学术研究提供高质量的基线，并在工业中找到有效的应用。该项目最终将采用上述模块和方法，并开发供行业专业人士使用的交互式工具。这将包括对有效可视化和界面设计的研究，并将在整个项目时间表中通过需求评估调查、讲习班和现场测试的各个阶段纳入迭代反馈过程。