|Data Driven Design – Retrofitting for a Low Carbon Future
How we design and size equipment needs a modern
approach as we retrofit with low carbon heating systems. All of that
BAS data you’ve been archiving can help.
SES Consulting Inc.
The era of real action on climate
change may finally be upon us! Last month’s climate change summit saw ambitious
new targets for reduced greenhouse gas emissions being announced for many countries,
including Canada and the US. Many
nations are aiming for reductions on the order of 40%-50% by 2030. With the building sector representing around a
third of total emissions, the next decade is set to see a flurry of activity
and investment directed at buildings to help achieve these targets.
A major focus of national climate
plans is a push to dramatically reduce the carbon intensity of our electrical
grids. Case in point, decarbonizing the electricity grid is a major focus of
the White House’s recently announced $2 trillion infrastructure plan. As a
result of all of this, commercial buildings will be increasingly pushed towards
replacing fossil fuel heating systems with electrified alternatives. Wherever
possible, this will mean the adoption of heat pumps as the most cost-effective
use of electricity for heating.
With that context in mind, let’s
consider how we normally go about sizing heating equipment for retrofits. More
often than not, the approach will be: Like for Like, rule of thumb, or “experience”.
A step up in accuracy might be to reference a handbook. Better yet might be a
proper load calculation or even a building model, if you’re lucky. All of these
approaches, some more so than others, have a major flaw in that they rely on a
lot of assumptions about how a building is working. On top of that, even a good
engineer following best practices will face pressure to have generous safety
margins. Why? No one wants to undersize a heating system and the headaches that
creates, so oversized systems are a nice piece of insurance. Especially when
the building owner is the one paying for it. Besides, more BTUs from gas or oil
fired boilers are pretty cheap, so no one gets too worked up about the extra
Switching to heat pumps shakes up
that paradigm in a big way. Below are rough costs pulled from our project
database for standard and high efficiency boilers along with air to water heat
Obviously these $ values will vary
somewhat location to location, but the overall trend is very clear: heat pumps
cost a LOT more than boilers. Also keep in mind this is just for the equipment
itself, add in the more complex installs, possible electrical and structural
upgrades, and the disparity can be even greater. Suddenly, an oversized heating
plant isn’t costing you $10,000 extra, it’s $100,000 or more.
But what is the best way to size
our equipment? Do the usual approaches even work well? Consider the results of
a study we did for a local university (pre-COVID) who were looking for low
carbon options for their domestic hot water systems. For one of their
buildings, we came up with the following results:
The last 2 values are based on
data we extracted from a gas sub-meter connected to the BAS. As you can see,
the actual building DHW load is much less than what conventional sizing
approaches would say you need. We routinely see the same results for heating
systems. This histogram shows the frequency that this boiler plant was
operating at different loadings. Significantly, the data showed that the plant
never got over about 25% loading.
Having data from gas, water and
BTU meters is great if you have them. But if you don’t (and most don’t), most
of this data can be pulled out of a typical BAS, either directly or through
constructing a virtual energy meter. If you have supply and return water temperature
sensors, you’re most of the way there already for most boiler plants. Data
quality is obviously important here, you want to make sure that your sensors
are calibrated! Water or air balancing on key systems might also be a good idea
if it’s been a while.
Unless you’re prepared to be very
patient, here’s where it really pays to have archived data handy. Data that has
probably been sitting there, just waiting for someone to come along and find a
use for it. Ideally, you want to be able
to look back over a full heating season or more, or at least make sure you’ve
got the full range of typical weather conditions covered. I’d also recommend
cross checking this data against another data source as a quality assurance
check, even monthly gas bills will do.
With this data in hand, we can
confidently move forward designing and optimizing equipment size against
overall energy use. For example, we’re finding that in a Pacific Northwest
climate, 80% or more of the heating load occurs when the outside temperature is
above freezing. Sizing heat pump equipment to the load at the minimum design
temp isn’t cost effective, so our heat pumps are usually sized to handle the
loads that cover 80% of the heating energy, relying on cheap BTUs from the boilers
for those infrequent peak loads. This kind of load matching with hybrid heating
sources is a big departure from how the industry has approached equipment
retrofits in the past, but I expect it to become the norm as we look for ways
to significantly cut the GHG emissions from our buildings.
Of course, you can go even deeper
into the data. You might decide to analyse your reheat valves and discover that
25% of your spaces represent 80% of your heating demand (based on a true
story). In a case like this, zone level retrofits with VRF or split heat
systems can be a more cost-effective way to reduce GHGs than trying to retrofit
the central plant.
It’s time to leave the 1950s when
it comes to how we approach mechanical retrofits. The push to retrofit
buildings with very costly low carbon heating systems over the next decade
conveniently comes at a time when our buildings are producing more data than
ever before. Let’s put that data to good use by optimizing how we design these new,
climate friendly, mechanical systems for our existing buildings.
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