Data-driven methods for operation and maintenance of commercial buildings

数据驱动的商业建筑运维方法

• 批准号: 516465-2017
• 负责人: Gunay, Burak
• 金额: $ 1.21万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development 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=719394
• 关键词: Data; driven; methods; operation; maintenance

Recent findings indicate that about 30% of the energy used in commercial buildings is wasted due to poorly maintained, degraded, and improperly controlled equipment and components. Given that indoor climate control in commercial buildings accounts for 13% of the total energy use and 11% of the CO2 emissions in Canada, optimizing their operation and maintenance represents great potential to reduce our environmental impact and to provide comfortable, healthy, and productive indoor environments.The objective of this research project is to develop data-driven decision support algorithms that guide better indoor climate control and maintenance decisions using the data inherent in building automation and control networks. New methods to detect and isolate component level faults before they begin to affect a building's comfort and energy performance will be developed. In addition, new methods to guide optimal start / stop times for heating and cooling equipment will be examined. The viability of using low-cost data streams in occupancy detection will be explored. The relationship between occupants' thermostat use behaviour patterns and their thermal comfort preferences will be investigated. The research approach entails field-scale data collection and analyses using existing controls and automation infrastructure of three office buildings in Carleton University and field trials.The proposed research project will make significant short-term and long-term intellectual, environmental, economic, and HQP contributions to Canada. New datasets, models, and methods will be created. Adoption of these methods by the industry partner, a Canadian building data analytics company CopperTree Analytics, will contribute to our knowledge-based economy. Wider usage of the algorithms developed in this research project will reduce the environmental and economic impact of commercial buildings. The HQP will work on data from real buildings, learn their systems and components, and their shortcomings; and conduct interdisciplinary research on building physics, indoor environmental quality, building performance simulation, and data-science.

最近的调查结果表明，商业建筑中约 30% 的能源因设备和组件维护不善、性能退化和控制不当而被浪费。鉴于商业建筑的室内气候控制占加拿大总能源消耗的 13% 和二氧化碳排放量的 11%，优化其运行和维护对于减少我们的环境影响并提供舒适、健康和高效的室内环境具有巨大潜力。该研究项目的目标是开发数据驱动的决策支持算法，利用建筑自动化和控制网络中固有的数据来指导更好的室内气候控制和维护决策。将开发新方法，在组件级故障开始影响建筑物的舒适度和能源性能之前对其进行检测和隔离。此外，还将研究指导加热和冷却设备最佳启动/停止时间的新方法。将探讨在占用检测中使用低成本数据流的可行性。将研究居住者的恒温器使用行为模式与其热舒适偏好之间的关系。该研究方法需要使用卡尔顿大学三座办公楼的现有控制和自动化基础设施进行现场规模的数据收集和分析以及现场试验。拟议的研究项目将对短期和长期的智力、环境、经济和总部规划产生重大影响对加拿大的贡献。将创建新的数据集、模型和方法。行业合作伙伴（加拿大建筑数据分析公司 CopperTree Analytics）采用这些方法将为我们的知识经济做出贡献。更广泛地使用该研究项目中开发的算法将减少商业建筑对环境和经济的影响。总部将处理来自真实建筑物的数据，了解它们的系统和组件以及它们的缺点；并开展建筑物理、室内环境质量、建筑性能模拟和数据科学等跨学科研究。