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

Rensselaer Polytechnic Institute, Troy NY

Investigators

Abstract

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.