Multi-level predictive control and diagnostic strategies for optimal operation of heating ventilating and air conditioning systems in buildings

建筑物供暖通风和空调系统优化运行的多级预测控制和诊断策略

• 批准号: 36380-2012
• 负责人: Zaheeruddin, Mohammed
• 金额: $ 1.46万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568726
• 关键词: Multi; level; predictive; control; diagnostic

Heating, Ventilating and Air conditioning (HVAC) systems consume nearly one half of the total energy used in modern buildings. The central issue in the quest for energy efficiency in buildings continues to be the design of improved control strategies for efficient operation of HVAC systems. This task is a challenging one since the building HVAC systems are large scale complex systems. As a consequence, it is very difficult for the operators to optimize the system operation by trial and error or locate faulty components manually. Inefficient operation coupled with faulty components increases energy consumption, shortens equipment life, and degrades indoor environment in buildings. With this as the motivation, a comprehensive research program is proposed here in the three key areas of research namely: multi-level control, fault diagnostics and optimal operation of whole building HVAC systems. It is proposed to develop multi-level predictive control strategies, fault isolation and fault tolerant control strategies and optimal operation strategies for the whole building HVAC systems to improve energy efficiency and thermal comfort in buildings. The developed strategies will be tested by computer simulations, by conducting experiments in a variable air volume HVAC test facility and finally by carrying out full scale tests in real building HVAC systems. The developed algorithms are modular in nature which facilitates their integration in the existing energy management control systems used in buildings. The overall approach combines energy efficiency, thermal comfort and sustainability measures in a multi-level control platform. This greatly improves the operational efficiency and energy performance of the whole building HVAC systems. The proposed research will lead to the development of control strategies that will have a direct and very substantial impact on the energy consumption levels in buildings.

采暖、通风和空调（HVAC）系统消耗了现代建筑物总能耗的近一半。在追求建筑物中的能源效率的中心问题仍然是设计用于HVAC系统的有效操作的改进的控制策略。这是一个具有挑战性的任务，因为建筑暖通空调系统是大规模的复杂系统。因此，操作员很难通过试错或手动定位故障部件来优化系统操作。低效率的操作加上故障部件增加了能源消耗，缩短了设备寿命，并使建筑物的室内环境恶化。以此为出发点，本文提出了一个综合性的研究计划，主要研究三个关键领域：多级控制、故障诊断和整栋建筑暖通空调系统的优化运行。 为提高建筑物的节能性和热舒适性，提出了建筑物空调系统的多级预测控制策略、故障隔离与容错控制策略和优化运行策略。所开发的策略将通过计算机模拟进行测试，通过在变风量HVAC测试设备中进行实验，最后通过在真实的建筑HVAC系统中进行全尺寸测试。所开发的算法是模块化的性质，这有利于他们在建筑物中使用的现有能源管理控制系统的集成。整体方法将能源效率、热舒适性和可持续性措施结合在一个多级控制平台中。这大大提高了整个建筑HVAC系统的运行效率和能源性能。 拟议的研究将导致控制策略的发展，这将对建筑物的能源消耗水平产生直接和非常重大的影响。