Data-driven methods for operation and maintenance of commercial buildings

最近的研究结果表明，商业建筑中使用的能源中约有30%是由于维护不善，退化和控制不当的设备和组件而浪费的。考虑到商业建筑的室内气候控制占加拿大总能源使用量的13%和二氧化碳排放量的11%，优化其操作和维护具有巨大的潜力，可以减少对环境的影响，并提供舒适，健康，和富有成效的室内环境。本研究项目的目标是开发数据-驱动的决策支持算法，使用楼宇自动化和控制网络中固有的数据指导更好的室内气候控制和维护决策。将开发新的方法来检测和隔离组件级故障，然后再开始影响建筑物的舒适度和能源性能。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。该研究方法需要现场规模的数据收集和分析，使用现有的控制和自动化基础设施的三个办公楼在卡尔顿大学和现场试验。拟议的研究项目将作出显着的短期和长期的智力，环境，经济和HQP贡献加拿大。将创建新的数据集、模型和方法。加拿大建筑数据分析公司CopperTree Analytics采用这些方法，将有助于我们的知识型经济。更广泛地使用本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。HQP将处理来自真实的建筑物的数据，了解它们的系统和组件及其缺点;并对建筑物理，室内环境质量，建筑性能模拟和数据科学进行跨学科研究。

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.