no/low-cost ways to drive savings today using real-time energy data · 2016-03-08 · no/low-cost...
TRANSCRIPT
Introduction
Most facilities staff and energy managers have two things in common: 1) they don’t have enough time, and 2) they don’t have enough money to spend on projects that would improve the operating efficiency of their buildings. This eBook is designed to help those weary champions of operational excellence by outlining five Energy Efficiency Measures (EEMs) that require little or no capital investment, improve (or at least are neutral to) occupant comfort, and help build momentum within a company that a savvy energy manager can parlay into funding for more capital intensive projects.
To make sure we’re all on the same page, we’re defining a “no/low-cost Energy Efficiency Measure” as a corrective action with a payback of less than two years. Even the most hard-nosed CFO has to like those economics. Each measure addresses one of the 3 energy cost drivers: how you buy energy, how much you use, and when you use it. As an example, an EEM may be defined by a activity inefficiency that’s easy to fix, such as identifying which buildings are energy abusers after-hours and making simple corrections to building management system (BMS) settings. Or, a suggested measure may combine a more thorough understanding of how you are billed for energy with the right tools to help you address those specific cost driver components (like in the case of demand charges).
The five EEMs covered in this eBook include:
1. Night, Weekend, and Holiday Setbacks
2. Smart Start-ups
3. Coasting
4. Demand Charge Management
5. Economizing
Importantly, all five of these energy efficiency measures are predicated on having access to your facility’s interval energy data. Why? Because simply looking at your last energy bill (what we consider a “rear-view mirror” approach) doesn’t translate into effective energy management.
In the following sections, we will outline key definitions and concepts, as well as provide specific user case studies. We will also demonstrate what the EEM looks like when displayed through 5-minute interval data.
Night,Weekend, and Holiday Setbacks
Optimal night, weekend, and holiday
setbacks top the list of most frequently
recommended energy efficiency measures,
both because they’re so common and
because they’re one of the easiest EEMs
to implement (typically at no cost). Whether
you are managing a schedule of occupied
versus unoccupied hours manually or through
a BMS, using real-time energy data to make
the most of night, weekend, and holiday
setbacks could be a useful EEM to pursue.
The premise of improving these setbacks is
to make sure that the energy consumption
for a building’s unoccupied time is reduced
to the bare minimum, typically to the night-
time baseload settings for more efficiently-run
buildings. Nighttime baseload is the minimum
amount of energy demand a building uses to
power systems that operate continuously
over a 24-hour period. Basically, it is the
energy required to operate the life-safety
and security systems, computer servers,
and outside/off-peak lighting. A large compo-
nent of a building’s nighttime baseload at a
commercial property, for example, is the
minimum HVAC operation needed to make
sure that the building’s pipes won’t freeze
and that the building can get back to the
occupied temperature setpoint the next
morning. Unlike most of the other compo-
nents of the nighttime baseload, which are
relatively consistent, HVAC load varies with
the outside air temperature, making it an
important aspect to monitor when determin-
ing if nighttime baseload readings are accu-
rate when establishing any type of setback.
For industrial or manufacturing facilities,
monitoring setbacks can be critical to
ensuring that supportive equipment such
as air compressors, exhaust fans, cooling
equipment, and lighting in inventory space is
shut down outside of operating hours.
Facility ExampleOptimal night, weekend, and holiday setbacks
are some of the most universal EEMs and
apply to almost all types of facilities, except
those that have 24x7x365 operations.
In this example, a recommendation related
to night setbacks was delivered to a large
owner/operator of a commercial real estate
facility. Though this example is specific to
night setbacks, the methodology is applicable
for weekends and holidays, too.
Definition and Background1
1
Challenge: Variable nighttime energy use during night setback
To check the efficacy of setbacks, analysts
usually track the nighttime baseload by
measuring a building’s energy demand after
all the occupants leave the building and
before the building starts up in the morning.
Baseloads for night setbacks should be
relatively consistent throughout a season.
Typically, any drastic changes in the baseload
represent some sort of inefficiency in the
building, such as overridden temperature
controls, accidentally disabled setback
controls, or malfunctioning equipment.
In this US-based case, the baseload was
inexplicably varying over a period of days in
October, as Figure 1.1 demonstrates.
Note how the building’s nighttime baseload
doesn’t consistently hit the same baseline
after the 26th of October. This indicates that
there might be a problem with the facility’s
night setback.
Solution: No-cost BMS programming fix
In order to gain a better understanding of why
the baseload was so unpredictable during the
night setback, evening hours were isolated
and compared to the outside air temperature.
We discovered that the building seemed to
be using much more energy than expected
anytime the outside air temperature dropped
below 40°F (4°C), as seen in Figure 1.2.
We showed the discrepancy to the facility’s
staff and upon further investigation, they
determined that a fan power box had been
inappropriately programmed to run throughout
the night, resulting in heat leakage and wasted
energy. Fixing the problem took fifteen min-
utes of a staff member’s time and resulted
in cutting the average demand during a night
setback by 32%, from 218 kW to 148 kW, as
seen in Figure 1.3.
2
Figure 1.1
Figure 1.2
EEM#1: Night, Weekend, and Holiday Setbacks
Facility Example
26 Oct 27 Oct 28 Oct 29 Oct
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SummaryWorking with the facility manager, the facility
cut its nightly energy use by one-third by
taking a closer look at energy interval data.
Not only did our analysts help identify that a
problem existed with the night setback, we
helped this facility use meter-level data to
diagnose and correct bugs in their BMS that
were causing it.
A BMS is run on setpoints and schedules,
which can result in energy inefficiencies if
inappropriately set, as in this example. BMS
schedules are also easy to override, making
changes often go unnoticed. Anomalies
and underperformance during previously
scheduled setback periods can go along
undetected with less than optimal settings
for weeks, months, or even years. Holiday
setback underperformance can be particularly
tricky because a given holiday might occur on
a different day of the week year-over-year, so
getting it right one year doesn’t mean that the
setback schedule will be right the next year.
Examining real-time energy data allows energy
managers to see irregularities in their data
over nights, weekends, and holidays to ensure
that they are taking greatest advantage of all
opportunities for setbacks.
3
Figure 1.3
we showed the discrepancy to the facility’s staff...fixing the problem took fifteen minutes of a staff member’s time and resulted in cutting the average demand during a night setback by 32%, from 218 kw to 148 kw.
EEM#1: Night, Weekend, and Holiday Setbacks
Summary
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Definition and Background4
2Another common way in which facilities can
increase their energy efficiency is by
implementing smart start-ups for their
HVACequipment. There are two types of
smart start-ups: delayed start and the
slightly-more-sophisticated optimal start,
which we’ll outline below. (Note: we discuss
how start-up strategies can help avoid some
types of demand charges, in chapter 4.)
First, a little background on how start-ups
typically work: not surprisingly, as soon as
a building’s HVAC system goes into setback
mode in the evening, the building’s inter-
nal space temperature begins to gradually
deviate from the occupied temperature
setpoint. For many facilities, HVAC systems
are controlled by a BMS to turn on well ahead
of scheduled occupied hours, to ensure that
the facility has reached its occupied (or con-
tracted) temperature setpoint well before the
first tenants are in the door. This approach
is fairly standard practice; energy managers
prefer to err on the side of caution when
it comes to tenant comfort and avoid
those hot/cold calls. What they may not
understand, however, is how much this
procedure is costing their business.
Now let’s talk about the smart start-up
options. A delayed start is exactly what it
sounds like: programming your BMS to start
your energy system as late as possible while
still hitting your individual standards for occu-
pant comfort or your contractual space condi-
tion obligations. An optimal start goes a step
further by taking into account seasonality
and changing weather conditions in a BMS’
automatic settings. Visibility into real-time
data, however, is essential for answering two
key questions: a) when exactly is that optimal
start-up time or point?, and b) how much will
an optimal start actually save you?
This EEM is second on our list because
in almost every case where the EnerNOC
analyst team has been engaged to examine
customers’ real-time energy data, this is one
of the first things they look at to find the
ultimate “low hanging fruit” of energy savings.
Let’s look at some examples.
SmartStart-ups
Smart Start-up Type 1: Delayed Start
Facility ExampleA delayed start is applicable for most facilities
with conditioned spaces–office buildings,
hospitals, schools, government buildings, etc.
(Even most industrial/manufacturing facilities
have some conditioned office space, so it is
applicable there, too, though it is unlikely that
it could impact a significant percentage of
overall energy spend.) The facility discussed
in the following example is a conditioned
space facility.
Challenge: Wasting energy during building start-up
By examining one full month of data,
EnerNOC analysts generated the report
in Figure 2.1 for a conditioned space facility
in the US to improve the efficiency of the
building start-up. The shaded grey region
is the tenant occupancy period. The blue
line represents existing demand and the
red line is the proposed demand. The
shaded green region is the estimated
kWh savings potential.
We can see from this report that while
the building is starting up at 5:00a.m., its
contracted occupancy period doesn’t begin
until 8:00a.m. It’s likely that the building
doesn’t need a full three hours to reach the
desired setpoint, so it’s a good candidate for
a delayed start-up.
Solution: Incremental delayed building start-up
Most conditioned spaces have specific
schedules programmed into their BMS.
These systems are designed to make sure
that the lights are on and the building is
heated or cooled during scheduled hours.
To close the three-hour gap highlighted in
this facility example, we recommended
gradually moving the start-up time of the
BMS in the mornings back in 15-minute
increments daily to see how the building
responds, with the goal of finding the least
amount of time required to achieve the
desired setpoint. This measured approach
resulted in approximately US$6,000 in
savings for the year and ensured no adverse
impact to occupant comfort.
If your building doesn’t have a BMS, a smart
start-up strategy can be employed by adjusting
your HVAC start time and walking around your
facility and manually taking temperature
readings. You should adjust your HVAC start
times in small intervals daily (try 15 minutes
at first) until you find the right start-up time
that meets your building’s needs, keeping
your peak demand window in mind.
This analysis can be run for every building in
a portfolio to see where a delayed start-up
could be most beneficial for each building.
The analysis should be rerun at least three
times a year–summer, winter, and during
shoulder seasons (spring/fall)–to ensure
that a delayed start makes sense for each
season. We discuss more about seasonal
variation with optimal starts below.
Smart Start-up Type 2: Optimal Start
Facility ExampleAn optimal start is a more sophisticated
approach to conserving energy during a
building’s morning start-up. As such, an
optimal start-up is ideal for medium or large
commercial real estate facilities, particularly
those with hands-on facilities managers and
a more advanced BMS. The example below
highlights a commercial real estate facility
with a sophisticated BMS.
Challenge: Building start-up energy requirements vary by season
Throughout the year, weather conditions vary
and, as a result, so do the thermal losses
and gains a building experiences overnight
and in the morning. A very obvious example
of this is when a building achieves a very
different internal temperature overnight during
the winter versus the summer.
Figure 2.2 is one facility’s start-up profile
before implementing an optimal start-up.
The lines represent demand (kW), while the
bars represent the number of cooling degree
days or heating degree days (HDD/CDD).
Notice how the building starts up at roughly
the same time, despite changing weather
conditions. This graph suggests that the facility
is likely wasting energy in the mornings by
not differentiating the building start-up time
by season and changing weather conditions.
Since this facility has a sophisticated BMS,
they are a good candidate for an optimal start.
Facility Example5
EEM#2: Smart Start-ups
Summary6
Figure 2.2 Figure 2.3
EEM#2: Smart Start-ups
Figure 2.1
=max =min=max =min
Solution: Varied BMS starting time based on seasonal conditions
Using about five months’ worth of energy
datafrom this US-based, EnerNOC analysts
determined the optimal start-up time for
each season.
For example, during the summer, the building
may reach 78°F (26°C) at night, and it may
take 1.5 hours to get from 78°F (26°C) back
to 72°F (22°C) in the morning. However, in
the fall, it may only reach 74°F (23°C) at
night, so it may only take 30 minutes to get
to 72°F (22°C) in the morning.
Figure 2.3 shows how the start-up time
of a building with optimal start controls
changes seasonally: the coldest day starts
the earliest and the mildest day starts the
latest. For this specific facility, these changes
represented approximately US$4,000 a year
in energy savings.
Automated controls as part of a sophisti-
cated BMS can facilitate optimal start-up.
If a facility doesn’t have optimal start-up
controls, energy managers can still imple-
ment an optimal start-up by carefully tracking
when the building starts up on a monthly
or even weekly basis, and adjusting based
on historical data and trends. This can be
achieved through inspection of charts like
those included here or by factoring in the
upcoming weather forecast for the next few
weeks or month.
SummaryBy taking a close look at real-time energy
meter data and monitoring weather
conditions, energy managers can gain a
more detailed analysis of a building’s energy
consumption and improve start-up practices.
Data driven energy efficiency leads to more
intelligent energy management and,
ultimately, greater savings. In the first
example, these changes represented
approximately US$6,000 in savings for the
year; in the second, roughly US$4,000.
Additionally, access to real-time energy data
allows energy managers to see the benefits
of staggering the start-up times of various
systems and machines, which reduces overall
energy demand in the mornings and can be
helpful in reducing demand charges, to be
discussed in a later chapter.
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Definition and Background7
Coasting3It’s only natural to follow smart start-up
practices with smart shutdown strategies,
so for EEM #3, we bring you Coasting 101.
HVAC and lighting systems are often kept on
well beyond the building’s occupied period
at the end of the day, resulting in wasted
electricity and money. Coasting trims the
operation of the controllable load (HVAC
and sometimes lighting) so that it shuts off
at or before the space is vacated,
lessening waste.
Shutting down mechanical heating/cooling
systems 15-30 minutes prior to the sched-
uled end of the occupancy window generally
goes unnoticed by tenants. A building’s
thermal mass will generally retain the
existing temperature +/- 2°F (-17°C) (i.e., it
will “coast” to its unoccupied temperature
setpoint). Studies have also shown that if the
venting system continues to circulate air (i.e.,
if people can “hear” the air conditioning or
heating on), they are even less likely to notice
such a small variation in temperature. This is
a very important point; the last thing we want
to do with any of our EEMs is create more
service calls for the building operations staff.
Facility Example
Coasting is a potential EEM for facilities
in pretty much any conditioned space with
occupied versus unoccupied HVAC/lighting
schedules. The scenario described below is
from US-based primary.
Challenge: Wasting energy at close of day
This facility was waiting to shut down its
HVAC system until the end of the occupancy
period. Figure 3.1 shows an analysis that
was conducted to demonstrate how much
energy could be potentially saved if the
facility shut down its HVAC shortly before the
end of the occupancy period and coasted for
the remainder of the occupied time.
In Figure 3.1, the shaded grey region is the
tenant occupancy period (when the tenants
are contracted to occupy the space). The blue
line is the existing demand and the red line
is the proposed demand. The shaded green
region is the estimated kWh savings potential.
Facility Example/Summary8
Solution: Gradual, 30-minute HVAC setback at end of day
We recommended that the facility inspect
the HVAC schedule and begin setting back
the HVAC system 30 minutes prior to the
scheduled end of the contracted occupancy
time. Our analysts also recommended that
the facility monitor the amount of lighting and
plug load left on as people vacated the space
at the end of the day to see if there was room
for improvement on either of those fronts,
as well.
Figure 3.2 shows the visual verification of
coasting to verify the results of this EEM.
This particular building was able to save
about US$14,000 annually by making the
small schedule adjustments at the end of
the day. Combined with other buildings in the
portfolio that made similar small scheduling
adjustments, the entire portfolio saved about
US$25,000 annually.
SummaryAlthough results may be small and variable
for each individual building, coasting can be
an effective strategy for lessening or
decreasing energy waste across a portfolio
of buildings. In this example, setting back the
HVAC system of a school building 30 minutes
prior to the scheduled end of the occupied
period helped save the school US$14,000
in that building alone (and nearly double
that amount when the coasting strategy was
implemented across other similar buildings).
This coasting example illustrates how access
to energy meter data allows energy
managers to monitor the tangible savings
garnered through minor adjustments to
building HVAC settings.
Figure 3.1
Figure 3.2
EEM#3: Coasting
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Definition and Background9
4Demand charge management may be fourth
on this list, but it is often ranked #1 as a
savings opportunity, depending on a) how
you’re billed for electricity, and b) how much
electricity your facility consumes at critical
times. There are different kinds of demand
charges, like Transmission System Use of
Network (TSUoN) charges, Time of Use (ToU)
tariffs, and individual facility peak demand
charges. The severity of the demand charge
can vary, with facility peak demand charges
- which are common in the US - sometimes
accounting for 30% of a total energy bill.
What is a peak demand charge?
In many cases, electricity use is metered
(and you are charged) in two ways: first,
based on your building’s total consumption
in a given month (kWh), and second, on your
building’s demand (kW), based on the highest
rate of consumption of your building during
the given billing period (typically a 15-minute
interval during that billing cycle).
To use an analogy, think about consumption
(kWh) as the number that registers on your
car’s odometer (how far you’ve driven), and
demand (kW) as what is captured on your
speedometer at the moment when you hit
your max speed. Consumption is your overall
electricity use, and demand is your peak
intensity, or maximum “speed.”
With residential buildings, these two charges
may appear as a combined charge (like all
tariffs, this varies), but because commer-
cial and industrial users have significant
variance in both consumption and demand,
these charges are often (but not always)
broken out. National Grid in the US explains:
“Some [commercial and industrial energy
users] need large amounts of electricity once
in awhile–others, almost constantly.” And
because electricity can’t be stored, meeting
these customers’ needs quickly becomes
complex and costly, requiring “a vast array
of expensive equipment–transformers,
wires, substations, and generating stations–
on constant standby. In some areas, all
customers are assessed a demand charge
to cover these costs, while in others, cus-
tomers who create this exceptionally high,
or “peak,” demand are then correspondingly
charged more for it.
Demand Charge Management
How are peak demand charges calculated?
Consumption is measured at a rate based
on kilowatt hours (kWh), and demand is
measured in kilowatts (kW). To understand
how this applies to your energy use, see the
two examples below.
Note that when demand is higher–i.e., using
more kilowatts and for a shorter time period–
demand charges will be higher versus using
the same total amount of kilowatt hours (kWh)
over a longer time period and at a lower inten-
sity. Overall consumption remains the same
between the two companies in the example,
but the amount each pays varies because of
demand charges.
Let’s assume these rates apply to both companies (in USD):
Electricity charge = $0.0437 per kWh
Demand charge = $2.79 per kW
Example 1: Company A runs a 50 megawatt
(MW) load continuously for 100 hours.
50 MW x 100 hours = 5,000 MWh
5,000 MWh = 5,000,000 kWh
Demand = 50 MW = 50,000 kW
Consumption:
5,000,000 kWh x $0.0437 = $218,500
Demand:
50,000 kW x $2.79 = $139,500
Total Charges: $358,000
Example 2: Company B runs a 5 MW load for
1,000 hours.
5 MW x 1,000 hours = 5,000 MWh
5,000 MWh = 5,000,000 kWh
Demand = 5 MW = 5,000 kW
Consumption:
5,000,000 kWh x $0.0437 = $218,500
Demand:
5,000 kW x $2.79 = $13,950
Total Charges: $232,450
Definition and Background10
EEM#4: Demand Charge Management
Definition and Background
Figure 4.1
using real-time energy data monitoring, we were able to see that the facility’s cleaning schedule was causing a spike in the facility’s peak demand charge. the crew was instructed to move the clean-ing schedule to outside the peak demand window, which lowered the facility’s…peak demand charge by approximately us$45,000 annually.
EEM#4: Demand Charge Management
11
For the same amount of kilowatt hours
used–i.e., at the same consumption level,
albeit at different intensities–Company A pays
significantly more in charges.
Depending on your rate structure, peak
demand charges can represent up to 30%
of your utility bill. Certain industries, like
manufacturing and heavy industrials, typically
experience much higher peaks in demand
due largely to the start-up of energy-intensive
equipment, making it even more imperative to
find ways to reduce this charge.
Facility Example/Summary
Regardless of your industry, taking steps to
reduce demand charges will save money.
Let’s take a look at how one US-based facility
boosted their bottom line by adjusting their
peak demand charges using real-time energy
data monitoring.
Facility ExampleWhether or not building scheduling and peak
demand management can help a facility cut
their energy bill depends largely on the build-
ing’s energy use patterns and the specific
rate structure offered by the utility. In the
case highlighted in this example, an industrial
facility had a steam turbine generator that
generated electricity for the facility.
Challenge: On-peak demand charges
Building owners often tweak their equipment
scheduling to balance the trade off between
consumption and demand charges. In this
particular example, the crew had to bring
the boiler (that supplied the steam for the
facility’s turbine) offline to clean it every night.
While cleaning the boiler, peak demand shot
up 400-500 kW each night, as illustrated
in Figure 4.1.
Upon reviewing the facility’s energy data,
analysts found that peak demand charges
were much higher than necessary because
the cleaning was scheduled to occur within
the facility’s peak demand window, the
on-peak hours during which peak demand
charges are most expensive. This problem
appeared to be a prime candidate for peak
demand management.
Solution: No-cost scheduling adjustment
Using real-time energy data monitoring, we
were able to see that the facility’s cleaning
schedule was causing a spike in the facil-
ity’s peak demand charge. The crew was
instructed to move the cleaning schedule to
outside the peak demand window, which low-
ered the facility’s demand during the on-peak
hours, and thus lowered its peak demand
charge by approximately US$45,000 annually.
SummaryVisibility into real-time energy data allowed
this facility to better its consumption pattern
and peak demand charges. By making a
no-cost scheduling adjustment, they were
able to lower their peak demand charge by
US$45,000 annually.
Energy managers can manage peak demand
charges by using real-time energy data to
monitor and adjust when they achieve their
peak demand–and also lowering the peak
demand actually reached during a billing
cycle. Some other examples of peak
demand management include starting up
the building’s systems before on-peak hours
or operating the most energy intensive
equipment at different times so all machines
aren’t running simultaneously, which makes
peak demand lower.
EEM#4: Demand Charge Management
12
Definition and Background13
5Economizing is when a facility’s HVAC system
uses outside air conditions to help condition
the building. One way to do this is by using
dampers that are designed to allow “free
cooling” with outside air. When the outside
air is cooler than the return air, the outside
air dampers are opened completely. When the
outside air is warmer than the return air, the
outside air dampers are closed.
This EEM is likely to occur when outside
temperatures are between 50°F (10°C) and
65°F (18°C), but that can vary depending
on facility construction and location.
See the example below to learn how we
used real-time energy data to take full
advantage of outside air conditions to
save energy and money.
Facility ExampleEconomizing is applicable for most condi-
tioned spaces. Sophisticated BMS systems
have this functionality built into them, and
therefore are good candidates for reviewing
the sequencing of the system and the
system’s setpoints. It’s common to find
a BMS that has deviated from its original
setpoints after gradual adjustments and
thus its economizing capabilities have been
impacted. This example highlights a commer-
cial real estate facility.
Challenge: Not using outside air to de-crease energy needed to condition space
The US-based facility in this example appears
to switch from heating to cooling its condi-
tioned space at 55°F (13°C) (called their
change point), as Figure 5.1 illustrates.
Since the building switches from cooling to
heating at approximately one temperature,
it’s apparent that this facility is not taking
advantage of optimal weather conditions
between 45°F (7°C) and 60°F (16°C). If
the facility was economizing properly, the
occupied period should reflect a relatively
flat line during that temperature bandwidth,
indicating that less energy is being used to
condition the space as compared to either
the heating or cooling period. Economizing
Solution: Leverage economizing capabili-ties to allow “free cooling” with outside air
Based on the real-time energy data analysis
illustrated in Figure 5.1, it appeared that
this US-based facility wasn’t fully using
its economizing capabilities. If it were, its
average hourly demand versus outside air
temperature (OAT) graph would look more
like the demand in Figure 5.2.
Note the relatively flat area between 40°F
(4°C) to 60°F (16°C) in Figure 5.2. This
facility has properly functioning dampers
and has economizing measures in place.
EnerNOC’s recommendation to the facility
from Figure 5.1 was to inspect the HVAC
setpoints for the economizer to fully take
advantage of “free cooling” with outside air
to reduce the energy needed to condition the
building. Sometimes the economizer malfunc-
tions due to stuck dampers, disconnected
actuators, or broken sensors, so we also
recommended a thorough parts inspection.
Although the savings potential varies from
facility to facility, a 300,000 square foot
conditioned space facility typically saves
between US$1,000-$4,000 on average from
economizing, depending on its fuel source.
Economizing is also helpful because it can
highlight equipment failures that would
otherwise go undetected, making it an even
more valuable EEM.
SummaryUsing real-time energy data, this facility was
able to see that it was not taking advantage
of outside air temperatures as much as it
could be to help cool its building, resulting
in a missed energy savings opportunity.
By adjusting settings within the economizer
to more fully take advantage of “free cooling,”
typical facilities can save US$1,000-$4,000
on average from this EEM.
Facility Example/Summary
Figure 5.1
Figure 5.2
EEM#5: Economizing
14
Outside Air Temp 10 20 30 40 50 60 70 80 90 100 (°F)
-12 -7 -1 4 10 16 21 27 32 38 (°C)
Outside Air Temp 10 20 30 40 50 60 70 80 90 100 (°F)
-12 -7 -1 4 10 16 21 27 32 38 (°C)
Low and no-cost energy efficiency measures
are a great way to either get started with
energy management, or to take your current
efforts to the next level. Having access to
real-time energy meter data is instrumental
in identifying numerous EEMs, including those
outlined in this eBook, as well as others.
Without interval energy data, energy managers
have to rely on sometimes months-old data
from their utility bills without visibility into daily
operations and energy use to try and track
down potential inefficiencies and anomalies.
We also acknowledge that you have limited
time to pore through all of this data and
manually conduct analyses, which is why
EnerNOC’s energy intelligence software
(EIS) provides visibility into how energy is
consumed and where it is being wasted,
and provides the tools – including powerful
analytics, reports, and dashboards – to help
you identify priority action. When that doesn’t
go far enough, our professional services team
can dig into your energy data to spot these
and other energy efficiency opportunities and
deliver them to you in a ranked list by ROI.
For more information, visit our website
at www.enernoc.com or contact a member
of our team.
If you’re interested in even more tips, tricks,
and energy management best practices, check
out our EnergySMART Blog to take your
energy efficiency management knowledge
to the next level.
Conclusion15
About EnerNOCEnerNOC, Inc. is a leading provider of energy intelligence software (EIS). EnerNOC unlocks the full value of energy management for thousands of customers worldwide by delivering a comprehensive suite of software applications and professional services that help users buy energy better, manage utility bills, reduce energy consumption, participate in demand response, and manage peak demand.
For more information, visit www.enernoc.com. Catch up on the latest best practices in energy management on our EnergySMART blog, energysmart.enernoc.com.
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