Data-Smart Building Case Studies

数据智能建筑案例研究

• 批准号: EP/V011936/1
• 负责人: Paul Ruyssevelt
• 金额: $ 25.75万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV011936%2F1
• 关键词: Data; Smart; Building; Case; Studies

The building sector accounts for about 40% of total final energy use and harbours the enormous potential to save energy and reduce CO2-emissions in a cost-effective way. Many of these cost-effective opportunities do not require significant capital outlay, relying instead on sound decision making and proficient implementation of building maintenance and operational control strategies. Indeed, many buildings are poorly commissioned from inception such that they never operate as designed. Even for well-commissioned buildings, the performance of systems might degrade over time, and this can go unnoticed resulting in poor performance. The extent of the opportunity is highlighted by Katipamula and Brambley (2005) who claim that "poorly maintained, degraded, and improperly controlled equipment wastes an estimated 15% to 30% of the energy used in commercial buildings." The importance of these issues in now widely acknowledged. For example, within the Mission Innovation Challenge 7 on Affordable Heating and Cooling a specific task on Predictive Maintenance and control Optimization (PMO) is foreseen. A key pre-requisite to achieve progress in such a task is access to high-quality contextual and metered data. In the UK, the Smart Meter Research Portal project seeks to create and make publicly available such a resource by linking contextual to smart meter data. Rich data sets are becoming increasingly available, but the question remains on how to maximise the insights and actionable information that can be extracted from these data.The work planned for this project seeks to explore the ways in which such datasets can be beneficially used to address the issue of PMO; in addition, it will seek to explore the benefits of more integrated building-level integration. The main premise is that contextual information together with highly granular data can be processed to identify performance degradations and inform predictive maintenance decisions. It then makes sense to seek to tune and optimise operation by more intelligent control strategies. Beyond technical challenges, for effective adoption of such new data-centric approaches, the value proposition needs to be identified for various stakeholders, and the identification of potentially viable business models, regulatory boundaries and procurement mechanisms. Around the world, good practice examples and evidence are appearing related to the benefits and practical application pathways towards Data-Smart Buildings.The scope of this work is potentially vast and cannot be addressed within a single project. The recently established IEA EBC Annex 81, Data-Driven Smart Buildings seeks to pool resources from around the world to create a critical mass of researchers then can address such a challenge. This project will support the UK's involvement and leadership on Subtask D of the Annex, on Case Studies and business models. The case studies to be collected will consider the logical steps on the journey from measurement (gathering data) to building management (actionable knowledge and building control). These aspects are to include latest research developments that span from: (i) data collection and data-modelling; (ii) data-driven modelling; (iii) capturing expert knowledge but also using Artificial Intelligence, to create data-driven applications and services, to; (iv) the utilisation and adoption of new services and business models. This approach follows the adage that 'you can't manage what you don't measure'. To ensure that the evidence is relevant, the work will cover a range of representative building typologies, climates and occupant applications. The findings will be communicated to stakeholders.

建筑业占最终能源使用总量的40%左右，在节约能源和以具有成本效益的方式减少二氧化碳排放方面具有巨大的潜力。这些具有成本效益的机会中有许多并不需要大量的资本支出，而是依靠健全的决策和熟练执行建筑物维修和业务控制战略。事实上，许多建筑物从一开始就没有得到很好的委托，以至于它们从来没有像设计的那样运行。即使是调试良好的建筑物，系统的性能也可能会随着时间的推移而下降，这可能会被忽视，导致性能低下。Katipamula和Brambley（2005年）强调了这一机会的程度，他们声称“维护不善、退化和控制不当的设备浪费了商业建筑中使用的能源的15%至30%。“这些问题的重要性现在已得到广泛承认。例如，在关于可负担得起的加热和冷却的使命创新挑战7中，预见了关于预测性维护和控制优化（PMO）的特定任务。在这一任务中取得进展的一个关键先决条件是获得高质量的上下文和计量数据。在英国，智能电表研究门户项目旨在通过将上下文链接到智能电表数据来创建并公开提供此类资源。丰富的数据集变得越来越可用，但问题仍然是如何最大限度地从这些数据中提取洞察力和可操作的信息。本项目计划的工作旨在探索如何将这些数据集有益地用于解决PMO问题;此外，它将寻求探索更集成的建筑级集成的好处。其主要前提是可以处理上下文信息和高粒度数据，以识别性能下降并为预测性维护决策提供信息。因此，通过更智能的控制策略来调整和优化操作是有意义的。除了技术挑战之外，为了有效采用这种新的以数据为中心的方法，需要为各种利益相关者确定价值主张，并确定潜在可行的商业模式、监管边界和采购机制。在全球范围内，有关数据智能建筑的好处和实际应用途径的良好实践实例和证据正在出现。这项工作的范围可能很广，无法在单个项目中解决。最近成立的IEA EBC附件81，数据驱动的智能建筑旨在汇集来自世界各地的资源，以创建一个临界质量的研究人员，然后可以解决这样的挑战。该项目将支持联合王国参与并领导附件关于案例研究和商业模式的分任务D。收集的案例研究将考虑从测量（收集数据）到建筑管理（可操作的知识和建筑控制）的过程中的逻辑步骤。这些方面将包括最新的研究进展，包括：（i）数据收集和数据建模;（ii）数据驱动建模;（iii）捕获专家知识，但也使用人工智能，以创建数据驱动的应用程序和服务;（iv）利用和采用新的服务和商业模式。这种方法遵循了一句格言：“你无法管理你不衡量的东西”。为了确保证据的相关性，这项工作将涵盖一系列具有代表性的建筑类型，气候和居住者应用。调查结果将传达给利益攸关方。

项目成果

Data Driven Smart Building Case Studies

数据驱动的智能建筑案例研究

• DOI: 10.5281/zenodo.7326671
• 发表时间: 2022
• 作者: Ruyssevelt P