Bayesian processes for calibration of building performance simulation models and optimisation of building energy systems design

用于校准建筑性能模拟模型和优化建筑能源系统设计的贝叶斯过程

• 批准号: RGPIN-2019-06188
• 负责人: Rysanek, Adam
• 金额: $ 1.97万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=743825
• 关键词: Bayesian; processes; calibration; building; performance

Since the landmark Paris Agreement on Climate Change was struck in 2015, international organisations, national governments, and global industry have started new efforts at developing policy pathways, incentive schemes, and technology options for an immediate reduction in building-attributed greenhouse gas emissions. Particularly in the highly-regulated building markets of Europe, North America, and East Asia, redevelopments of building codes and standards have increasingly looked towards the use of building performance simulation (BPS) tools to facilitate decision-making and code-compliance for energy-efficient, low-carbon building design and retrofits. As this is ongoing, there remains two persistent barriers to the success of BPS-based decision-making in the real world: 1) the frequently-encountered performance gap represented by the discrepancy between expert-driven model predictions of building energy use and actual measurements, and 2) the lack of inexpensive and accessible techniques for BPS model calibration. The long-term objective of this project is to transform conventional building design practises by enabling the explicit representation of prediction uncertainty in building design problems that use BPS tools for decision-making. Specifically, the project proposes to research, develop, and validate: 1) a new approach to calibration of transient BPS models using Bayes theorem, 2) a new approach to stochastic optimisation of building design using Bayesian processes, and 3) a new approach to surrogate modelling of future building performance using Bayesian processes. To facilitate the project's long-term objective, the following short-term objectives will be delivered in sequence: (1) develop and execute an approach to assimilating building construction information and performance benchmarking data from at least 18 candidate commercial, residential, and institutional buildings in the Metro Vancouver region; (2) develop and execute an expert elicitation process to solicit any prior knowledge of uncertainty related to each building's construction information; (3) establish a best-practise approach to handling uncertainty within specific BPS sub-processes, such as occupancy prediction, building energy demand simulation, and energy supply systems modelling (4) systematically test and validate a new approach to Bayesian calibration of BPS models for each candidate building; (5) undertake an experimental investigation of the suitability of Bayesian BPS surrogate models to be applied for stochastic optimisation of building design and/or robust prediction of future building energy and environmental conditions. The overall outcome of the work will be the validation and dissemination of a new data-assisted simulation schema for predicting and optimising the energy consumption of buildings in Canada under prevailing technical and economic uncertainties.

自2015年具有里程碑意义的《气候变化巴黎协定》达成以来，国际组织、各国政府和全球工业界开始了新的努力，制定政策途径、激励计划和技术方案，以立即减少建筑造成的温室气体排放。特别是在欧洲、北美和东亚高度监管的建筑市场，建筑规范和标准的重新制定越来越多地关注使用建筑性能模拟（BPS）工具，以促进节能、低碳建筑设计和改造的决策和规范合规性。由于这是正在进行的，仍然存在两个持久的障碍，以成功的基于BPS的决策在真实的世界：1）经常遇到的性能差距，由专家驱动的模型预测的建筑物能源使用和实际测量之间的差异，和2）缺乏廉价和可访问的技术BPS模型校准。该项目的长期目标是通过明确表示使用BPS工具进行决策的建筑设计问题中的预测不确定性来改变传统的建筑设计流程。具体而言，该项目建议研究，开发和验证：1）使用贝叶斯定理校准瞬态BPS模型的新方法，2）使用贝叶斯过程随机优化建筑设计的新方法，以及3）使用贝叶斯过程替代未来建筑性能建模的新方法。为了促进项目的长期目标，将按顺序实现以下短期目标：（1）制定和执行一种方法，以吸收温哥华地区至少18座候选商业、住宅和机构建筑的建筑施工信息和性能基准数据;（2）开发和执行专家启发过程，以征求与每个建筑物的施工信息相关的不确定性的任何先验知识;（3）建立最佳实践方法，以处理特定BPS子过程中的不确定性，例如占用预测，建筑物能源需求模拟，和能源供应系统建模（4）系统地测试和验证用于每个候选建筑物的BPS模型的贝叶斯校准的新方法;（5）对贝叶斯BPS替代模型的适用性进行实验研究，以应用于建筑物设计的随机优化，以及/或对未来建筑物能量和环境条件的稳健预测。这项工作的总体成果将是验证和传播一种新的数据辅助模拟模式，用于在当前技术和经济不确定性的情况下预测和优化加拿大建筑物的能源消耗。