Wireless Occupancy Detection to Improve Building Energy Efficiency
Wireless Occupancy Detection to Improve Building Energy Efficiency
批准号:
1336824
负责人:
Nicholas Kirsch
金额:
$29.97万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-01-15 至 2018-08-31
中文摘要
1336824(Fu).建筑物是美国最大的能源消耗者,因此,创造绿色建筑物可以大大帮助这个国家?我们迫切需要提高能源效率和可持续性。该项目的目标是检测和估计建筑物中的居住人数,以控制建筑物的能源消耗系统,从而提高能源效率。传统的占用传感器检测占用者的存在,其中单个人被平等地视为一组人。对于照明等建筑控制,这些基于存在的方法就足够了。然而,控制,如通风,加热和冷却,可能会根据房间内的居住者人数而波动很大。与传统传感器通过运动或体温检测居住者的物理存在不同,研究人员建议通过感知现有的手机通信来检测居住者。最近发表的一项研究报告说,99%的人携带手机,甚至闲置的手机也经常与手机信号塔通信。通过感应建筑物内的手机信号,研究人员提出,可以准确地确定手机(及其携带者)的数量和位置。然后,占用数据将被提供给建筑自动化系统,以通过调整不同的建筑系统(通风、加热/冷却等)来提高能源效率。相应地本研究包括三个目标:基于蜂窝通信的居住者跟踪,分析和预测的随机占用节能控制策略,并在真实的建筑原型测试。在第一个目标中,研究人员将使用多个蜂窝电话控制通道交通传感器来识别每个乘客的位置。该项目的第二个目标集中在蜂窝信号数据的数据融合和分析。研究人员将开发算法,根据频谱感知网络确定占用者的数量和位置。利用占用率跟踪数据,研究人员将开发测试建筑物占用率的随机模型。这些模型将用于预测本项目下一阶段的占用水平。在最后一个目标中,调查人员的目标是实施新的控制策略,利用全面的占用数据来提高楼宇控制系统的效率。实验将在十个校园建筑物,以实现与大学建筑物管理合作提出的方法。该系统的成功将提高建筑物的可持续性,并通过提供实时占用数据来改变建筑物设计和运营的领域。此外,所提出的非侵入性占用检测系统可以在诸如安全、救援工作(定位受害者)和老年人护理(养老院中的居民跟踪)等领域中找到用途。PI将把研究的不同方面纳入电气/计算机和土木工程课程(无线通信系统和绿色建筑设计)以及UNH的可持续性双专业。来自不同背景的学生将作为一个团队工作,并接触到不同的,跨学科的研究课题。
英文摘要
1336824 (Fu). Buildings are the largest consumers of energy in the United States and, therefore, creating green buildings can significantly aid the country?s pressing need to enhance energy efficiency and sustainability. The objective of this project is to detect and estimate the number of occupants in buildings to control building energy-consuming systems and thereby increase energy efficiency. Conventional occupancy sensors detect the presence of occupants, where a single person is treated equally as a group of people. For building controls such as lighting, these presence-based methods are sufficient. However, controls, such as ventilation, heating and cooling, may fluctuate considerably depending on the number of occupants in a room. Instead of detecting the physical presence of occupants from movements or body heat as in conventional sensors, the investigators propose to detect occupancy by sensing existing cellphone communications. A recently published study reported that 99% of individuals carry cellphones and even idle cellphones communicate routinely with cell towers. By sensing cellphone signals inside buildings, the investigators propose that the number and locations of cellphones (and their human carriers) can be accurately determined. The occupancy data will then be provided to building automation systems to improve energy efficiency by adjusting different building systems (ventilation, heating/cooling, etc.) accordingly. This research includes three objectives: cellular communication-based occupant tracking, analysis and prediction of stochastic occupancy for energy efficient control strategies, and prototype testing in real buildings. In the first objective, the investigators will use multiple cellular phone control-channel traffic sensors to identify the position of each occupant. The second objective of the project focuses on data fusion and analysis of the cellular signal data. The investigators will develop algorithms that determine the number of occupants and position based upon the spectrum-sensing network. With occupancy tracking data, the investigators will develop stochastic models of occupancy in test buildings. Such models will be used to predicate occupancy levels for the following phase of this project. In the last objective, the investigators aim to implement new control strategies that leverage the comprehensive occupancy data to improve efficiency in building control systems. Experiments will be conducted in ten campus buildings to implement the proposed methods in collaboration with university building management. The success of the proposed system will improve building sustainability and potentially change the fields of building design and operations by providing real-time occupancy data. Moreover, the proposed non-intrusive occupancy detection system may find use in areas such as security, rescue efforts (locating victims) and elderly care (resident tracking in nursing homes). The PIs will incorporate different aspects of the research into Electrical/Computer and Civil Engineering courses (Wireless Communication System and Green Building Design) as well as the Dual Major in Sustainability at UNH. Students from different backgrounds will work as a team and be exposed to diverse, interdisciplinary research topics.
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IUCRC Planning Grant: University of New Hampshire: Center for Digital Factory Innovations (CDFI)
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批准号:2113892
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项目类别:Standard Grant
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资助金额:$2.0万
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财政年份:2021
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负责人:Nicholas Kirsch
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依托单位:
海外基金