Benchmarking operation of commercial buildings through text-mining maintenance work-orders

在加拿大，商业建筑的室内气候控制占总能源使用量的13%，二氧化碳排放量的11%；商业建筑约30%的能源消耗是由于维护不善、降解和控制不当的设备和部件造成的。因此，用于建筑运营和维护的数据驱动的分析工具有可能减少我们对环境的影响，并提供舒适、健康和高效的室内环境。本研究项目的目标是开发算法，通过工单管理系统中的文本挖掘来对建筑系统和部件的维护性能进行基准测试。工单是建筑物中信息保存的传统形式。工单管理系统包含操作员对暖通空调系统及其部件的维护例行程序和故障模式的描述，尽管是大型无定形文档。因此，它们很少被用来提取有关暖通空调常见故障及其发生频率的信息。将开发新的算法，从文本挖掘工作订单管理系统中提取有用的信息。该算法将识别顶级系统和部件级故障模式，开发部件级故障率模型，并为建筑系统及其部件引入故障模式和影响分析工具。拟议的研究项目将为加拿大做出重大的智力、环境、经济和HQP贡献。将创建新的数据集和方法。行业合作伙伴，加拿大最大的物业管理公司Bentall Kennedy采用这些方法，将有助于我们的知识经济。在此研究项目中开发的算法的广泛使用将减少商业建筑对环境和经济的影响。HQP将处理真实建筑的数据，了解它们的系统和部件以及它们的缺点；并对建筑性能和数据科学进行跨学科研究

Indoor climate control in commercial buildings accounts for 13% of the total energy use and 11% of the CO2emissions in Canada; and, about 30% of the energy used in commercial buildings is wasted due to poorlymaintained, degraded, and improperly controlled equipment and components. Therefore, data-driven analyticaltools for building operation and maintenance have the potential to reduce our environmental impact and toprovide comfortable, healthy, and productive indoor environments.The objective of this research project is to develop algorithms that will benchmark the maintenanceperformance of building systems and components through text-mining within work-order managementsystems. Work-orders are the traditional form of information keeping in buildings. The work-ordermanagement systems contain operators' descriptions of maintenance routines and failure patterns in HVACsystems and their components, albeit as large amorphous documents. Consequently, they are seldom used toextract information about common HVAC faults and their occurrence frequencies. New algorithms will bedeveloped to extract useful information from text-mining work-order management systems. The algorithmswill identify top system and component-level failure modes, develop component level failure rate models, andintroduce failure modes and effects analysis tools for building systems and their components.The proposed research project will make significant intellectual, environmental, economic, and HQPcontributions to Canada. New datasets and methods will be created. Adoption of these methods by the industrypartner, Canada's largest property manager Bentall Kennedy, will contribute to our knowledge-based economy.Wider usage of the algorithms developed in this research project will reduce the environmental and economicimpact of commercial buildings. The HQP will work on data from real buildings, learn their systems andcomponents, and their shortcomings; and conduct interdisciplinary research on building performance anddata-science