PFI-TT: Smart Climate Control in Shared Workspaces for More Personalization and Efficiency

PFI-TT：共享工作空间中的智能气候控制，实现更多个性化和效率

基本信息

批准号：
1827546
负责人：
Koushik Kar
金额：
$ 20万
依托单位：
Rensselaer Polytechnic Institute
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827546&HistoricalAwards=false
关键词：
PFI TT Smart Climate Control

项目摘要

The broader impact/commercial potential of this PFI project is in attaining significant energy savings in building operations, and in creating more comfortable and personalized work environments for its occupants. Energy efficiency measures in the buildings not only provides a means to reduce energy related costs, it also provides tremendous opportunity to reduce greenhouse gas emissions. Further, the technology that this project aims to develop and evaluate can also create a more personalized workspace for human occupants in a shared space, leading to better wellness and increased productivity of employees in indoor shared workplaces. The education and outreach goals of the project will be enhanced through cross-disciplinary training of a graduate student researcher, and involvement of undergraduate students in the experimental evaluation of the BEES system in a Smart Conference Room facility at RPI. The project team will actively engage with industrial partners and potential clients towards maximizing its commercialization potential. We believe that the proposed technology has considerable future potential for creating new jobs in emerging areas such as Smart Buildings, Internet-of-Things, and Artificial Intelligence.The proposed project seeks to develop a data-driven learning and integrated control solution for heterogeneous HVAC elements in shared office spaces, with the aim of providing more personalized thermal environments to occupants and minimizing overall energy usage. Even with the high cost of operating the current HVAC systems, occupant dissatisfaction with thermal environments in workplaces have been highlighted by several recent studies. This project seeks to address this issue through the integrated application of i) data-driven learning of the indoor environment, and ii) integrated control of heterogeneous HVAC elements that typically exist in such shared office spaces. Firstly, it will utilize data driven modeling of complex spatiotemporal dynamics of the physical environment, and the dependency between the controls available and the environmental variables. Secondly, it will utilize the data-driven model towards integrated control of the heterogeneous HVAC elements associated with the space to attain differentiated temperatures across the space as desired by the occupants. The product of this project will be a software prototype that will operate in conjunction of sensor and IoT devices, and networked HVAC elements. This integrated HVAC control technology will be evaluated for technical performance and commercial viability in a smart conference room.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该PFI项目的更广泛的影响/商业潜力在于在建筑运营中获得大量节能，并为其居住者创造更舒适和个性化的工作环境。建筑物中的能源效率措施不仅提供了减少与能源相关成本的方法，而且还提供了减少温室气体排放的巨大机会。此外，该项目旨在开发和评估的技术还可以为共享空间中的人类居住者创建一个更个性化的工作空间，从而提高健康状况，并提高室内共享工作场所员工的生产率。该项目的教育和外展目标将通过对研究生研究人员的跨学科培训以及本科生参与RPI智能会议室设施的蜜蜂系统的实验评估来增强。项目团队将积极与工业合作伙伴和潜在客户互动，以最大程度地发挥其商业化潜力。我们认为，拟议的技术具有在智能建筑，互联网和人工智能等新兴领域创造新的工作的巨大潜力。拟议的项目旨在为共享办公空间中的异质HVAC元素开发一个数据驱动的学习解决方案，以便为占用的热量环境提供更多个性化的热量环境，以便为占用的热量提供更多个性化的热能环境和最小化的能量。即使运行当前HVAC系统的成本很高，最近的几项研究强调了乘员对工作场所热环境的不满。该项目旨在通过i）数据驱动的室内环境学习的集成应用来解决此问题，ii）对这些共享办公空间中通常存在的异质HVAC元素的集成控制。首先，它将利用数据驱动的物理环境的复杂时空动力学以及可用控件和环境变量之间的依赖关系。其次，它将利用数据驱动的模型来整合与与乘员所需的空间相关的空间相关的异质HVAC元件的整合控制。该项目的产物将是一个软件原型，将通过传感器和IoT设备以及网络的HVAC元素运行。该集成的HVAC控制技术将在智能会议室中评估以进行技术绩效和商业可行性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Peak Power Reduction for HVAC Operations in Multi-unit Commercial Buildings

减少多单元商业建筑 HVAC 运行的峰值功率

DOI：
10.1109/isgt49243.2021.9372176
发表时间：
2021
期刊：
2021 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT
影响因子：
0
作者：
Raza Naqvi, Syed Ahsan;Kar, Koushik;Bhattacharya, Saptarshi;Chandan, Vikas
通讯作者：
Chandan, Vikas

Experimental Evaluation of Data-Driven Predictive Indoor Thermal Management

数据驱动的预测性室内热管理的实验评估

DOI：
10.1145/3307772.3331031
发表时间：
2019
期刊：
Proceedings of the Tenth ACM International Conference on Future Energy Systems
影响因子：
0
作者：
Tariq, Zaid Bin;Imam, M. H.;Kar, Koushik;Mishra, Sandipan
通讯作者：
Mishra, Sandipan

Optimizing HVAC Operations in Multi-unit Buildings for Grid Demand Response

优化多单元建筑中的 HVAC 运行以实现电网需求响应