Occupancy-based Adaptive HVAC Control for Energy Efficient Residential Buildings

根据加拿大统计局的数据，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）被认为是气体排放的最重要因素。该项目旨在加强住宅暖通空调系统，以智能占用检测/识别为基础，优化能源消耗。虽然已经提出了几种技术/传感器来应对这一挑战，但成本、准确性以及与现有建筑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。最近，现代家庭中价格合理的传感器的数量和类型明显增加。例如，太阳辐射传感器用于检测室内照明状况。红外和超声波传感器被用作接近和运动传感器。体重传感器在一些家庭中变得越来越普遍。一些先进的暖通空调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先进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。拟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的麦克风传感器可能用于开发占用地图。雷达和基于硅mems的麦克风传感器通过音频波束形成结合雷达目标存在检测提供远场声波捕获。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。在培训阶段，将收集占用率和身份数据以及可用的传感器测量值。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用于检测占用情况。然后，基于雷达/ mems的麦克风可以使用语音识别来识别乘员。

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.