PFI-RP: Data-Driven Services for High Performance and Sustainable Buildings
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The broader impact/commercial potential of this PFI project will lead to the creation of a truly new breed of building services companies that can provide guaranteed building performance. The results from this proposal will contribute to both building energy efficiency research and practices. Beyond energy conservation, outcomes of the project also have an important societal benefit in advancing the role of a skilled workforce in conjunction with automation. In addition, the proposed effort builds an innovation ecosystem which spans the supply chain for building services and building analytics and automated intelligence suppliers. The proposed research will fulfill an important role in sustainable environments and will enable services with significant economic and human impact. While the building service industry is the focus here, the results obtained have the potential to lead to a better understanding of other human-centered services driven by big data, and will influence the development of novel service platforms applicable to urban operations, such as city-wide transportation control, power grids, and public health, in which system-wise fault discovery and recovery have to be achieved through dynamic spatio-temporal data streams of varying quality and coverage. The proposed project of Building Doctor's Medicine Cabinet (BDMC) service platform, empowered by novel multi-resolution (temporal and spatial) data analytics, high-dimensional robust modeling, and human-centered interface design, will help building doctors, who are engineers and technicians working in the building service industry, to effectively troubleshoot building problems and to identify systematic and prognostic solutions. BDMC will integrate building datasets from in-situ and control system measurements, knowledge from building doctors, and leverage the patterns and anomalies discovered on this data to (1) diagnose and prognose whole building problems with greatly reduced engineering labor input, false alarms and false dismissals, 2) identify building system hierarchy and develop data-driven energy models; and (3) provide human-centered data visualization and feedback (fault impact analysis and prioritization). The proposed effort will transform the current labor-intensive building service industry into a smart service industry. The main outcomes from this PFI-RP project are algorithms and codes that can be licensed to the industry to be integrated with their existing market products. 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.