Daikin Integration to BACnet, Modbus, KNX, WIFI, Mobile Apps
|Let the People Rule
Harnessing occupancy data from IT for optimizing HVAC operation
SES Consulting Inc.
Ph. D, P.Eng, CEO, Building Scientist
Sensible Building Science
building automation system (BAS) knows a lot about what’s going on in
its building. It knows the status of all of the HVAC equipment, space
temperature throughout the building, outside conditions, etc. But for a
system ostensibly designed primarily to achieve occupant comfort, your average BAS
knows precious little about said occupants. How many people are in your
building? Where are they? When do they arrive? When do they leave? Not
knowing the answer to these questions represents a severe handicap in
trying to optimize HVAC operation. A lack of good occupant data usually
results in a very conservative approach that results in long system run
times under the assumption that someone could be there. Anyone who
has walked through an office at 3pm on a Friday before a long weekend
and witnessed the rows of empty desks knows that these assumptions are
often way off base.
Your typical motion detector can provide a bit of insight into this, but their usefulness in answering these questions is restricted by the simplicity of a binary signal and typically very limited coverage. Expanding this coverage to provide comprehensive monitoring comes at a high cost, and that still wouldn’t provide any real insight into the number of occupants or very precise information of their location.
It turns out that, in some buildings, there is a source of occupant data hiding in plain sight. Enterprise wi-fi systems often contain location information for all devices on the network. Patent-pending technology developed by Sensible Building Science (SBS) creates a “Bridge” between the wi-fi system and the BAS and provides the sort of detailed occupancy information that we have been seeking. Once the data connections have been established, this virtual occupancy metering (VOM) is straightforward to map onto the HVAC zones, giving actionable data on where people are in the building. Better yet, due to the centralized nature of enterprise wi-fi systems, a single point of connection can potentially give you occupancy data across multiple buildings or an entire university campus.
Universities turn out to be a particularly good application of this technology for a number of reasons. These include near universal wi-fi coverage, lots of people concentrated into particular areas, erratic usage schedules, and ubiquitous presence of smartphones and laptops interacting with the wi-fi network. For a clearer picture of what the deployment and integration of this looks like, we can use the example of a project that our team is currently in the midst of at a smaller university campus.
In this case, the SBS Bridge connected Cisco’s CMX solution on the IT side to the university’s BACnet BAS. The project hardware deployment was relatively quick with the Bridge being installed in a single afternoon. After that, six core buildings were brought online with every floor fully mapped to the HVAC system, along with data transmission to a pilot building, in just two weeks. The next step was to figure out exactly how to best use this data to optimize HVAC optimization. Not all areas benefit equally from integrating occupancy control, so we wanted to figure out where we would have the most impact and start there. In addition to prioritizing the rollout of the new control strategy, we also wanted to use the data to provide a level of assurance to the university that significant energy savings could really be achieved.
To do this, HVAC models of each AHU system were created and calibrated against real world schedules and energy use. Then, several weeks of VOM occupancy data were brought into the models to predict the impact of moving to occupancy based control on HVAC operation and energy consumption. This analysis looked at the impact of occupancy based control at the air handler level, floor level, and zone level. Here’s a sample result:
Modelled Occupancy Based
Start/Stop Control for Air Handling Unit
In some cases, for example, floor isolation, achieving the savings would require physical changes to the HVAC system like adding new isolation dampers. In other cases, like AHU or zone occupancy based scheduling, the only changes required were adjustments to BAS programming. The results of this analysis revealed which systems occupancy based control would be most effective on. Having the model results on hand to guide deployment allowed the university to better understand the opportunities, prioritize rollout, and show quick wins. Following deployment, there was the opportunity to compare real world performance to the models in one of the buildings. This found the AHU run times to be within 10% of the model’s predictions generally. Collectively, the impact of the operation changes, once fully implemented, will reduce the energy consumption of the affected buildings by around 5%.
Going forward, there is the opportunity for implementing even more sophisticated occupancy based strategies. For example, using VOM as a supplement to, or in place of, CO2 sensors for demand controlled ventilation. This has advantages in terms of response time; VOM is faster than gas sensors, such as CO2, because it takes time for gas pollutants to build-up in a room. There is also the potential to apply machine learning algorithms so that you not only know where people are in real time but also where they’re likely to be in the future. AHUs can be activated just before people arrive to make sure a space is comfy and has fresh air, or be allowed to turn off early and have the building “coast” prior to the Friday afternoon exodus.
addition to gaining energy savings from optimized HVAC control, there
are other applications of this technology which may offer even greater
value. VOM coverage is very extensive and can cover all floor
areas, not just areas in-sight of hardware sensors. This means that it
can be used for space utilization planning, workforce performance
improvements, and a host of new applications. This, of course, of
course goes back to the old “3-30-300” rule. Looking at this occupancy data
through another lens, relative to total capacity instead of absolute
numbers, you get another perspective altogether. The chart below from
this previous SBS case study demonstrates this nicely.
case for using this data for optimizing space utilization in a
university setting is so compelling that, in at least one case, the
impetus for deploying the Bridge technology is originating from the
registrar’s office rather than the facilities department. There
is even the potential to have a positive impact on occupant
productivity, getting at the ever elusive “300”. A small example of how
this could be achieved is by using occupancy data to prioritize space
conditioning (and by extension comfort) for the areas with the highest
Harnessing existing occupancy data from an enterprise wi-fi system in this manner brings together IOT and big data analysis in a very practical application that offers a lot of value to the end user. The powerful combination of abundant data that is easy to access not only gives you a superior means of control but also allows you to predict outcomes ahead of time. By eliminating much of the guesswork and sketchy assumptions that we’ve relied on in the past, building owners can have confidence that their investments will pay off in terms of reduced energy bills, improved comfort, and gaining a powerful new lens on space utilization. Buildings that actually know something about their occupants are simply better buildings.
Before signing off, just a plug for a couple of sessions that Brad will be participating in at Realcomm/IBCon, coming up in San Diego later this month and the June preview of the event:
For more information on SBS and the work they’re doing, please have a
look at Stefan’s May interview.
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