At the time of this project the Department of Defense (DoD) occupied an estimated 276,770 facilities throughout the world, valued at more than $585 billion and comprising 2.2 billion square feet. The scale of DoD’s physical presence is reflected in its energy bootprint. In 2016, DoD consumed an estimated 198,031,000 metric million British thermal unit, roughly 57%, of the U.S. Federal Government’s total energy budget for the same year. The utility and operational data generated by such a scale of operations are enormous. Without a unified system to automatically collect, normalize, and present these data in a meaningful way, DoD energy and facility managers are left with the task of doing this manually. The objective of this project involved implementation of a software-based toolset enabling site operators with the capability to make effective, real-time data-driven decisions to reduce operating and energy expenses while monitoring additional conditions such as occupant comfort.

The User Interface serves to add analytics capabilities and provide a normalized interface for managing disparate systems. Core functionality is predicated on data access, normalization and management.

The technology costing framework takes several factors into consideration to determine the total value invested to deliver product and service. The scope and detail of each project is considered when applying the framework to accurately assess value input against value output, resulting in a tailored “best support model” for each site. These factors include data connection method, application programming interface/driver development, size of site, complexity of site, required training, and consultative support needed.

During this demonstration, the energy savings for all identified opportunities was estimated to be greater than 9% across the portfolio of buildings within the project, though not all these opportunities have yet been implemented. 

Numerous recommendations were made regarding incorrect or excessive equipment operation. These factor into the estimated energy savings, but also present less quantifiable results realized in reduced maintenance. Unfortunately, due to atypical occupancy, the COVID-19 pandemic, consistent data quality and granularity issues, reductions in peak energy demand and work order criticality were not able to be completely assessed.