US Military Buys in on Big Data Building Efficiency

Large-scale building efficiency has traditionally been a very manual process involving a lot of guesswork that ultimately leaves a lot of unknowns that ends up bleeding money that could otherwise be saved. One company, Retroficiency, says big data and analytics aims to turn this around with a system that can scale to hundreds of thousands of buildings, and the U.S. Army and Navy are buying in.

Buildings are responsible for 40% of the total US energy consumption, says the company, which also says that 30%-50% of that energy is wasted. While large organizations are very aware of the overhead that their operations cause and the potential for savings through programs to streamline and reduce their energy consumption, these processes are laborious, take weeks and months – and tracking what worked and what didn’t work is often nothing short of guessing.

Massachusetts-based Retroficiency says it’s cutting through this fog with a building efficiency intelligence platform that enables energy service providers to target the right buildings, engage customers with compelling analytics, and then convert those analytics into projects that can be tracked and verified for savings.

What’s more, it claims it can do all this without ever needing to go on site. Using the Retroficiency algorithms and the right data (asset data and/or interval consumption data), the company says it’s able to do push-button energy modeling that determines how each building in a portfolio is consuming energy by its end uses. The algorithms then compare the building’s current consumption to its optimal consumption and offers recommendations on how to get there.

The more data that a company has on its buildings and assets, the quicker and more detailed a report is able to be produced. But even with limited information, Retroficiency says it can provide physics-based simulations that can be useful. What’s more, rather than having energy auditors canvassing buildings and hoping the data they are collecting is useful, Retroficiency is able to tell auditors what specific data needs to be collected to improve its analysis.

The U.S. Army and Navy have both bought into Retroficiency’s energy modeling platform, according to a recent report in FCW. With such budgetary creatures as “the sequester” hanging over their heads, as well as a political environment struggling to come to grips with spending, military organizations are keen to find ways to save a buck. According to the report, the Army and Navy will be using Retroficiency to “identify energy savings in more than 650 worldwide facilities, with a target of making half of all Navy buildings net-zero energy by 2020, producing as much energy as they consume.”

Per FCW:

The analytics software can then examine combinations of more than 2,000 potential optimization measures – everything from glaring issues like HVAC renovations to subtle operational changes – providing automated outputs. Buildings within a portfolio can be ranked in terms of energy consumption and efficiency, allowing stakeholders to get the best bang for their buck in optimizing them.

Retroficiency hopes this collaboration will serve as an introductory use case that can spread through the rest of the US federal government, which is estimated to occupy somewhere around 500,000 buildings worldwide, and spends $7 billion each year on basic energy consumption for those buildings.

And that’s just the beginning. According to a report released by the Rockefeller Institute last year, there is over $1 trillion in energy savings that can be achieved over a 10-year period in the U.S., given the right tweaking. This represents a huge opportunity for Retroficiency and other players in the utilities arena looking for a piece of the action.

So while the big data technology trend is often knocked for its tendency for runaway hype, it’s easy to see its potential to change the economic landscape when considering the potential of this type of technology and what happens when it spreads. In the meantime, it’s worth looking at the Retroficiency processes and considering how they apply to other analytic endeavors.

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