Using component 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组件替代模型

• 批准号: 580451-2022
• 负责人: Evins, RalphRP
• 金额: $ 1.46万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749198
• 关键词: Using; component; surrogate; models; integrated

The buildings sector represents a significant portion of global energy consumption and greenhouse gas emissions. The design of buildings is a complex, dynamic, multi-disciplinary domain with long life-cycles and broad environmental impact. Simulation to support performance-based decision-making for building design and operation has evolved into an established practice, although traditional methods have struggled to adapt to the demands of net zero energy and emissions targets, especially in extending these often "premium services" to underserved and marginalized communities. Machine learning techniques can be used to train surrogate models that emulate discontinuous, multi-modal problem spaces with greater flexibility and computational efficiency than their high-fidelity counterparts. This research will apply Systems Decomposition, aided by qualitative study, to develop a conceptual framework of core functional requirements. Based on core research activities in the Energy in Cities lab at the University of Victoria, Surrogate models trained to fit components of the technical problem space will be aligned with the over-arching framework, and built up from high-fidelity simulations. This will create a powerful research platform suited for efficiently exploring some of the most pressing challenges that occur during early stage net zero building design, such as validation in the presence of significant uncertainties, advanced system configurations and complicated technical solutions, and demanding, non-traditional performance requirements capturing diverse community needs. Furthermore, through collaboration with the industry partner, tools and workflows will be developed that are grounded in practical application on real projects.

建筑业占全球能源消耗和温室气体排放的很大一部分。建筑设计是一个复杂的、动态的、多学科的领域，具有较长的生命周期和广泛的环境影响。模拟以支持基于性能的建筑设计和运营决策已发展成为一种既定做法，尽管传统方法难以适应净零能源和排放目标的要求，特别是在将这些往往是“优质服务”扩展到服务不足和边缘化社区方面。机器学习技术可用于训练替代模型，这些替代模型模拟不连续的多模态问题空间，具有比高保真对应物更大的灵活性和计算效率。本研究将应用系统分解，辅以质性研究，发展核心功能需求的概念架构。基于维多利亚大学城市能源实验室的核心研究活动，经过训练以适应技术问题空间组成部分的替代模型将与过度扩展框架保持一致，并从高保真模拟中建立起来。这将创建一个强大的研究平台，适合有效地探索早期净零建筑设计中出现的一些最紧迫的挑战，例如在存在重大不确定性的情况下进行验证，先进的系统配置和复杂的技术解决方案，以及满足不同社区需求的苛刻的非传统性能要求。此外，通过与行业合作伙伴的合作，将开发基于真实的项目实际应用的工具和工作流程。