Next generation fault detection, evaluation, and correction algorithms for HVAC control systems

适用于 HVAC 控制系统的下一代故障检测、评估和校正算法

• 批准号: 576809-2022
• 负责人: Gunay, BurakHB
• 金额: $ 1.46万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=748941
• 关键词: Next; generation; fault; detection; evaluation

Commercial building heating, ventilation, and air conditioning (HVAC) systems in Canada account for 7% of Canada's total energy use and are responsible for 6% of Canada's total GHG emissions. Growing evidence in the literature indicates that about 30% of the energy used by commercial building HVAC systems is wasted due to controls hardware and software faults. In partnership with an established Canadian building smart building energy data analytics software company, CopperTree, this project will eliminate much of this waste through algorithms that will automatically identify, evaluate, and correct these faults. Specifically, the project will develop autoencoder-based fault detection and diagnostics algorithms for sensor faults, surrogate models trained from a large database of energy model simulations for fault evaluation capabilities, and algorithms for auto-correction of faults emerging from inappropriate technician overrides or hardware issue. The algorithms will be developed by using the building performance simulation (BPS) tool EnergyPlus and its Python energy management system environment as a sandbox. Later, they will be tested in a living-lab facility at Carleton University for measurement and verification.Outcomes of the project include a suite of algorithms enhancing data analytics-driven building energy management software solutions. Development and demonstration of the algorithms will be disseminated via journal and conference papers. Three graduate students will be trained with advanced skills in controls and automation of HVAC systems, BPS, and data analytics. The algorithms are expected to reduce heating and cooling energy use in commercial buildings by 20-30%. While these benefits will be immediately available to 2,600 buildings currently using the partner's cloud-based smart analytics software, the benefits will scale to many of Canada's half a million commercial and institutional buildings.

加拿大的商业建筑供暖、通风和空调（HVAC）系统占加拿大总能源使用量的7%，占加拿大总温室气体排放量的6%。越来越多的证据表明，商业建筑HVAC系统所使用的能量中约有30%是由于控制硬件和软件故障而浪费的。该项目与加拿大建筑智能建筑能源数据分析软件公司CopperTree合作，将通过自动识别、评估和纠正这些故障的算法来消除大部分浪费。具体而言，该项目将开发基于自动编码器的传感器故障检测和诊断算法，从用于故障评估能力的能量模型模拟的大型数据库中训练的代理模型，以及用于自动纠正因技术人员不适当的覆盖或硬件问题而出现的故障的算法。这些算法将使用建筑性能模拟（BPS）工具EnergyPlus及其Python能源管理系统环境作为沙箱进行开发。随后，他们将在卡尔顿大学的一个生活实验室设施中进行测试，以进行测量和验证。该项目的成果包括一套增强数据分析驱动的建筑能源管理软件解决方案的算法。算法的开发和演示将通过期刊和会议论文传播。三名研究生将接受暖通空调系统，BPS和数据分析控制和自动化方面的高级技能培训。这些算法预计将使商业建筑的供暖和制冷能源使用量减少20- 30%。虽然这些好处将立即提供给目前使用合作伙伴基于云的智能分析软件的2，600座建筑物，但这些好处将扩展到加拿大50万座商业和机构建筑中的许多建筑物。