There is a fast-approaching time when the way we assess energy efficiency today will seem, well… quaint. “Remember the days,” our future selves will reminisce with a chuckle, “before the per-occupant energy rating was developed for commercial spaces? We were really grasping at straws!”
Real-time data is the key to unlocking that bold new future, and increasingly, networked lighting controls with high-density occupancy sensors promise to get us moving towards it.
The roundabout nature of innovation ensures that technology originally designed to maximize the efficiency of commercial building lighting systems would prove to be just the tool needed to accurately quantify occupancy data. In fact, networked lighting control platforms from companies like Enlighted, Philips Lighting and nLight are now being built around sensors—not the other way around.
The critical pivot came when the focus on lighting controls shifted from entire rooms to a per-fixture basis. Placing sensors in every light has been shown to deliver substantially better energy savings when compared to long strings of lights controlled by just one sensor.
“Efficiency” can be a funny word, sometimes construed to have more to do with the bottom line than actual effectiveness in-use. But fixture-based sensors actually better tailor lighting to the needs of individual occupants, too. The subtleties of spaces, like how much ambient light comes in from windows at a given hour of any day, can now be taken into account. The result is better lighting that can even be tailored to screens and electronic devices.
Of course, there is a secondary benefit, too. Installing lighting controls on each fixture creates a dense grid of sensors, yielding occupancy and use data on an extremely granular level.
A Fixture-Based Future
Up to present moment, the standards for estimating building populations have been anything but exact—interviews with facilities staff, surveying human resources data or conducting in-building counts. Even when staff counts are available, only individual managers often know travel schedules and what percentages of employees frequently work off-site. To think that these are the methodologies to which we’ve been relegated; we might as well be carrying torches for light, recording notes on stone tablets!
The obvious—and painful—truth is that these approaches are anything but accurate. Especially when it comes to identifying a basis for important economic and energy decisions.
With fixture-based sensors we can tell when occupants of a set floor or individual space arrive each morning, how many people work in each area of a building on a typical day and even when specific areas are underutilized. Aggregated across an individual floor or an entire building, this data records actual real-time occupancy levels.
All of this is important because building features alone do not determine total energy use. What’s going on inside the building, or more precisely, who is in the building, where and when, also play a huge role.
For example, if Building A is occupied from 6am to 10pm each day, while Building B only occupied from 9am to 5pm, it is reasonable to expect the A to use more energy.
Similarly, population density—or how many people occupy a building per square foot—can have a measurable impact on building energy use. As buildings adopt the WeWork model and pack more occupants into smaller spaces, the increased densities result in more body heat that must be cooled, higher plug loads like computers consuming power and on aggregate, noticeably more energy consumption than lightly occupied spaces.
It is crucial to know not just the total energy use, but also the portion of consumption that is driven by occupancy. In fact, accurate occupancy levels and space use readings for buildings have been long standing obstacles to fair energy comparisons of like facilities.
Getting Closer to the Bullseye
As more and more buildings install and track profiles from high-density networked lighting control systems, it is becoming possible to develop highly accurate occupancy metrics. As more comparative data come online, it will make it possible to adjust or calibrate the total energy use of buildings based on a fair and representative space use multiplier.
This will go a long way towards creating an equal playing field from which to rank the energy use of buildings with different occupancy levels accurately. For businesses and lessees this translates to having a relevant comparative scale to use when evaluating the energy performance of available real estate.
If the result is a reliable variable that can more readily factored into balance sheets and bottom lines, a more competitive marketplace for energy efficient commercial buildings could perhaps be the most important benefit in the end. That means a bigger incentive for efficiency upgrades for property owners, and more energy savings and fewer carbon-emissions across the board.
All, you might say, because somebody changed a lightbulb.
EUI Based on Total Meter Data
Space Use Multiplier
Occupancy Adjusted EUI
EUI (Energy Use Index) – 1,000 British Thermal Units/Square Feet (kBtu/ft2)
Photo Credit: Shaun Dunmall via Flickr