The recent history of building opera- tions management has been defined by the steady improvement of the
tools that operators and engineers can use
to inform decision-making. Building information modeling and other analytical models,
whether data or physics-based, have had
significant positive impacts on the industry.
Nevertheless, the American Council for an
Energy-Efficient Economy found that most
commercial air conditioners in the U.S. are
oversized by 25-50%.
The progress hasn’t been sufficient. A
shift is needed from the “art” of intuition,
assumptions and rules of thumb to the “
science” of empirical evidence driving statistically significant conclusions.
The Internet of Things (Io T) has unlocked
the potential to collect real-time empirical data about the individual components
that make up buildings. With data, analytics software can focus on benchmarking,
comparisons, and identifying patterns and
anomalies from observed performance.
Measuring and analyzing actual performance is necessary to continue to eliminate
waste in capital investments, maintenance
and repairs, and energy consumption.
Even robust building management
systems (BMS) that use continuous data
inputs to directly control equipment usage
are prone to waste through intuition
and assumptions. Improperly configured
BMS are believed by the Office of Energy
Efficiency & Renewable Energy to account
for 20% of building energy usage (about 8%
of total energy usage in the U.S.).
Let’s follow the lives of two commercial
air conditioning units.
The first one was installed and maintained
according to industry best practices. When
designing the system, the engineers used
rule of thumb calculations to ensure that it
guarantees enough cooling capacity to satisfy tenant requirements. As is common, the
system is 25% oversized.
The preventative maintenance schedule,
based on the manufacturer’s recommendations, dictates that the unit be serviced
once per year in early spring before the
After installation, tenants grumble that
the indoor temperature varies wildly when
it’s hot out. They find that when the air
conditioner kicks in, it gets very cold quickly
and then gets warm before jumping down
again. They also notice that it seems to stay
humid even when the air is cold.
Eventually, the operators get a complaint
from a tenant during the summer that its
space isn’t cooling at all. After an investigation, operators uncover that the unit has
been short cycling. They check the levels of
refrigerant, test the thermostat to ensure it’s
reading correctly and is appropriately placed,
and make sure the low-pressure control
switch and compressor are working properly.
Everything appears to check out, so the
operators determine the unit is likely oversized and is cooling the space too quickly,
cycling on and off quickly to maintain the
desired temperature. While the solution is to
replace the unit, there’s no room in the capital expense budget, so the operations team
decides there’s nothing they can do except
increase the amount of preventative maintenance checks during the cooling season.
For the rest of its life, the unit wastes
energy by running when the building is
unoccupied and outside air temperature is
relatively low. After 15 years, the unit fails
entirely, and a replacement is required.
Benchmarking for the
Now let’s look at the life of the second air
conditioning unit. It was installed and maintained using empirical data from circuit-level
electrical demand sensors.
When designing the system, the engineers used equipment-level benchmarking
to determine the right unit by considering
factors such as make and model, climate,
sizing and occupancy schedule. The benchmark was built by tracking and recording
millions of machine hours of air conditioning
units across the portfolio and aggregating
data from other equipment in the same
region and building vertical.
As the manufacturer recommends, the
operators perform a preventative mainte-
nance check in the early spring before the
cooling season. In addition, the operators
receive notifications when the electrical
demand of the unit indicates that conditions
necessitate servicing the unit. When the unit
inevitably needs repair, a fault detection
alert directs operators to the unit, avoiding
an investigation and enabling them to fix
the system before tenants notice.
If at any point, the system configurations
are changed to a suboptimal schedule or
balance point, the operators receive a notification that uses inferred occupancy and
weather data to prescribe the optimal settings. The unit lasts its full 20-year lifetime
before a replacement is required.
What’s the Difference?
The second scenario results in reduced
costs and improved tenant comfort. It also
requires continuously tracking electrical
consumption at the circuit level. This is an
investment that that pays for itself when all
the avoided costs are added up:
■ Capital investment costs: Lowered by
right-sizing equipment rather than paying
for unnecessary capacity.
■ Maintenance costs: Lowered by reducing
the number of unplanned maintenance
work orders, eliminating the time spent
investigating tenant complaints and
avoiding arbitrary additional maintenance
■ Energy costs: Lowered by maintaining
the optimal schedule and set points
instead of letting performance drift.
■ Net present value of money: Because
future money is less valuable than
money today, value is added by delaying
equipment replacement for five years.
■ Tenant experience: Improved by ensuring
normal air quality and by proactively
addressing issues, potentially affecting
The cost of an Io T-based equipment energy tracking solution is generally an order of
magnitude lower than a traditional building management system. Although these
systems can’t control equipment like a BMS,
the data-driven decision making enabled by
these solutions present an attractive investment, whether there's a BMS in place or not.
Experienced operators and engineers will
always be necessary to ensure that systems
run properly, but should favor hard data
over assumptions. The more we can direct
building operations from an art to a science,
the healthier our indoor environment will
be, the easier operators’ jobs will be and the
more profitable real estate will become.
Connell McGill is the CEO and Co-Founder of
Enertiv, a data analytics company focused on
streamlining building operations.
Efficient Building Operations
USE DATA TO ENSURE YOU ARE MAXIMIZING EQUIPMENT USAGE