SBIR Phase I: Big Data Analytics for Facility Operations and Management

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to help owners and operators of commercial and institutional buildings to improve resource allocation by analyzing data from built infrastructure to enable smarter decision-making supported by detailed, measureable, real-time knowledge. By automatically integrating building information that is stored using various software applications and formats, this innovation enables owners and facilities managers to efficiently search for information and respond to emergency and failures, and proactively plan for operation and maintenance tasks. This innovation also applies artificial intelligence to automatically conduct big data analysis and identify opportunities to improve energy efficiency and operating performance of assets and indoor environment. Organizations can not only save operating budget by reducing equipment failures and energy waste, but also improve the quality of life and productivity for occupants. This Small Business Innovation Research (SBIR) Phase I project is aimed at developing middleware technology to automatically integrate and analyze both structured and unstructured data from facilities design and operations. Facilities maintenance and operating is the longest phase in the life-cycle of buildings, accounting for more than 60% of the total cost of ownership. Owners and facilities managers are faced with the challenges of efficiently managing aging and crowded building infrastructure to extend the life of assets and control costs. However, fragmented and under-analyzed building information results in most maintenance work being conducted reactively to address problems that have already caused significant loss or waste. The vision of this innovation is to develop a fully commercialized software package to enable facilities managers to be more proactive in improving building occupant comfort, aligning limited resources where they have the most significant impact, and reducing wasted energy through optimized mechanical controls. This project aims to demonstrate the conceptual feasibility of using big data analytics and machine learning to revolutionize facilities operating and maintenance decisions. The results from this applied research will include algorithms and methods to combine structured data with field collected unstructured data into qualitative and quantitative output appropriate for improved decision making.