Occupancy-based Adaptive HVAC Control for Energy Efficient Residential Buildings

针对节能住宅建筑的基于占用的自适应 HVAC 控制

• 批准号: 514484-2017
• 负责人: Atia, MohamedMaherMohamed
• 金额: $ 1.82万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640039
• 关键词: Occupancy; based; Adaptive; HVAC; Control

Per Statistics Canada, households continued to be the largest users of energy accounting for 24.0% of nationalenergy use. Heating, ventilation, and air-conditioning (HVAC) has been singled out as the most importantcontributor to gas emissions. This project aims at enhancing residential HVAC systems to optimize energyconsumption based on intelligent occupancy detection/identification. While several technologies/sensors havebeen proposed to address this challenge, the cost, accuracy, and the seamless integration with existing buildingautomation infrastructures remain major challenges especially for homes where cost and complexity are keyfactors. Recently, there is a noticeable increase in number and types of affordable sensors in modern homes.For example, solar radiation sensors are used to detect room lightening condition. Infrared and ultrasoundsensors are used as proximity and motion sensors. Weight sensors are becoming common in several houses.Some advanced HVAC systems provides level of carbon dioxide. The availability of these sensors creates anopportunity to develop cost-effective advanced occupancy detection and identification technology to enableoptimized adaptive residential HVAC systems. The proposed project will adopt a sensor fusion approach totackle the problem. In one possible scenario, radar and silicon MEMS-based microphone sensors may be usedto develop occupancy maps. Radar and silicon MEMS-based microphone sensors provide far field soundwaves capture by audio beam-forming combined with radar target presence detection. With the increasingnumber of other available sensors, artificial intelligence and machine learning can be used to apply sensorfusion. The proposed system will have two phases; training phase and identification phase. In training phase,occupancy and identity data will be collected along with available sensor measurements. Data will be modeledusing a Hidden Markov Model. In identification phase, first, the available sensor measurements will be used todetect occupancy. Then, the radar/MEMS-based microphone can be will be used to identify the occupantsusing voice recognition.

根据加拿大统计局，家庭仍然是最大的能源用户，占全国能源使用量的24.0%。供暖、通风和空调（HVAC）已被挑选出来作为气体排放的最重要贡献者。该项目旨在增强住宅暖通空调系统，以基于智能占用检测/识别来优化能源消耗。虽然已经提出了几种技术/传感器来解决这一挑战，但成本，准确性以及与现有楼宇自动化基础设施的无缝集成仍然是主要挑战，特别是对于成本和复杂性是关键因素的家庭。近年来，现代家庭中可负担得起的传感器的数量和类型显著增加。例如，太阳辐射传感器用于检测房间照明条件。红外和超声波传感器被用作接近和运动传感器。重量传感器在许多房屋中变得越来越普遍。一些先进的HVAC系统提供二氧化碳水平。这些传感器的可用性为开发具有成本效益的高级占用检测和识别技术创造了机会，以实现优化的自适应住宅HVAC系统。建议的项目将采用传感器融合的方法来解决这个问题。在一种可能的情况下，雷达和基于硅MEMS的麦克风传感器可用于开发占用地图。基于雷达和硅MEMS的麦克风传感器通过音频波束形成与雷达目标存在检测相结合来提供远场声波捕获。随着其他可用传感器数量的增加，人工智能和机器学习可以用于应用传感器融合。拟议的系统将分两个阶段：培训阶段和鉴定阶段。在训练阶段，将沿着收集占用和身份数据以及可用的传感器测量结果。数据将使用隐马尔可夫模型建模。在识别阶段，首先，可用的传感器测量将被用来检测占用。然后，基于雷达/MEMS的麦克风可以用于使用语音识别来识别乘客。