Utilizing Facility Data to Identify Energy Improvements

Asset Management

By Michael Grussing, Ph.D., P.E., M.SAME, and Jay Tulley, M.SAME

By leveraging overlapping data streams through sources like BUILDER SMS, asset managers gain a more comprehensive view of facilities—leading to deeper insights, more effective planning, and new key performance indicators.
With BUILDER SMS now widely implemented across the Defense Department, the next step should be leveraging facility data to develop more holistic analyses and comprehensive facility data strategies. U.S. Air Force photo by 2nd Lt. Christine Saunders.

The Department of Defense is the largest energy consumer not just within the federal government but across the United States—and 32 percent of that power is consumed by installations at an energy cost of more than $4.2 billion annually.

With a footprint of 2.1-billion-ft² spread over 250,000 buildings worldwide, many of which were constructed before modern efficiency standards, these facilities present a huge demand on energy sources. Reducing this energy demand through building upgrades can result in operational cost savings, improved security posture, more resilient installations, and positive impacts on climate.

With SMS data now collected across the majority of the Defense Department’s facilities portfolio, there is a detailed dataset accessible on more than 200,000 buildings, comprised of over 10 million individual component assemblies, 21 million inspection observations, and 5 million images.

These energy efficiency improvements require targeted investments at a time when installation funding is already limited—and competing resources for sustainment, restoration, modernization, new construction, and demolition take precedent. The solution to this problem, however, should not be viewed as a choice between energy efficiency and facility sustainment. Rather, it should be viewed holistically as an opportunity to improve each simultaneously. To do that requires better analytics, and that, in turn, requires accurate, up-to-date, and relevant data.

An enormous amount of data already exists about military facilities. Vast amounts of information are captured each day through facility condition assessments, space planning assessments, energy audits, and installation energy and water plans. While each of these support a slightly different purpose, a significant amount of data overlap exists. And yet, with all these different assessments, some information gaps are still present that make a holistic facility analysis challenging.

A comprehensive facility data strategy is important. It shows how the different datasets are interrelated and can complement each other, as well as where certain data gaps still exist.

Analytical Tools

In 2013, the Office of the Secretary of Defense issued a memorandum for adoption of sustainment management systems (SMS). Since then, BUILDER SMS has been widely implemented across the defense enterprise—with the bulk of data collection, consisting of facility component inventory and condition assessment, now been completed. The next steps have included condition analysis and reporting, work needs identification, and long-range planning, which are all built-in capabilities of the legacy SMS tools and the new enterprise platform. Looking beyond those key capabilities, other uses of SMS are starting to emerge, including its role in facility energy analytics.

Extensive Inventory. The wealth of data included in SMS spans structured information like component inventories and condition metrics as well as unstructured data such as inspection comments and images. With SMS data now collected across the majority of the Defense Department’s facilities portfolio, there is a detailed dataset accessible on more than 200,000 buildings, comprised of over 10 million individual component assemblies, 21 million inspection observations, and 5 million images.

With this rich dataset, we not only know the use, age, and size of buildings in the department’s real property, but we have detailed information about the systems and components within all those buildings. This level of fidelity includes component types, capacities, age, condition, and remaining service life. This dataset provides a compelling potential to support a range of facilities and infrastructure analytics and decision-making, particularly when combined with other key data sources.

Merging Data Together

The Defense Department has compiled a large and growing dataset of facility energy consumption. As an example, the U.S. Army is actively monitoring and recording electric and gas data for more than 15,000 buildings. This utility data can be used to construct a generalized picture of how a building is performing from an efficiency standpoint. However, when integrating the detailed system and component asset information from BUILDER, we can then begin to attribute the general building performance to specific components and equipment.

As we better understand the correlation between the building’s material makeup and its overall energy performance, the information from BUILDER such as component type, quantity, capacity, age, and condition becomes increasingly valuable. Add to this other data streams such as weather, occupancy density, and building control system sensors, and the ability to target specific issues in an asset becomes even more refined. While we can look at overall energy consumption as just part of a facility’s primary performance metrics, better data from BUILDER and these other sources allows us to develop new metrics and key performance indicators, such as chiller or boiler capacity per square foot, building kW/ton of cooling, or building peak kW per electrical panel capacity.

As we better understand the correlation between the building’s material makeup and its overall energy performance, the information from BUILDER such as component type, quantity, capacity, age, and condition becomes increasingly valuable.

Leveraging Automation. Machine learning can play a key role as we synthesize, process, and analyze data to derive stronger correlations and optimal weightings across key performance indicators. By training the models on the data that we do have, we can leverage BUILDER and these other data sources to develop better energy models and forecast energy consumption rates for the buildings that do not have energy meter data. This allows facility staff and energy managers to make more informed data-driven decisions about sustaining, restoring, and modernizing assets most effectively, while incorporating efforts related to electrification, deep energy retrofits, and energy resiliency.

These improved insights and models should result in better operations and maintenance, project planning, and energy management.

Leveraging Data

Recently there has been an enormous amount of work done to understand what data can be leveraged to identify opportunities in initiatives like electrification, decarbonization, and deep energy retrofits. As the policies supporting these initiatives take shape, they can have ripple effects as installations try to comply.
While the window to identify project opportunities and develop justification packages can be narrow, it can also open pathways for additional resources to support facility sustainment, restoration, and modernization projects that address certain key initiatives. By continuing to address these questions and discuss the data requirements and policy development necessary to better position and streamline similar future efforts, the facility asset management community can aid in moving to better tools and data products to support truly holistic lifecycle asset management.

Michael Grussing, Ph.D., P.E., M.SAME, is Research Civil Engineer and Jay Tulley, M.SAME, is Research Engineer, Construction Engineering Research Laboratory – U.S. Army Engineer Research & Development Center. They can be reached at michael.n.grussing@erdc.dren.mil; and jay.tulley@usace.army.mil.

Article published in The Military Engineer, September-October 2024

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