utilizing data to maximize performance contract results

CMTA, INC. | WHITEPAPER | AUG 2020

Effectively trending the right data and acting on it enhances all phases of a performance contract, from initial benchmarking all the way through occupancy, and the result is top tier performance.

Effectively trending the right data and acting on it enhances all phases of a performance contract,

from initial benchmarking all the way through occupancy, and the result is top tier performance.

There is certainly a lot to be learned from physically observing systems. However, one can learn

many intricacies of a facility and its systems by simply monitoring and analyzing data output and

taking appropriate action. All data tells a story, but the story is only worth-while if it is listened to.

Data is priceless when actively used to drive decisions throughout the development and execution of

a guaranteed energy savings performance contract and no firm has a stronger track record of data

driven analysis and results tracking than CMTA.

9519 Civic Way Prospect, KY 40059

502.409.4062 cmta.com

UTILIZING DATA TO MAXIMIZE PERFORMANCE CONTRACT RESULTSWHITEPAPER | AUGUST 2020

PERFORMANCE CONTRACTING IS TRENDING

In order to understand how data trending may be effectively used throughout the life of a

guaranteed energy savings performance contract (GESC), one must first understand the process of

these projects. The typical, basic progression of a performance contract is as follows: benchmarking,

initial walkthroughs, scope of work development, construction and commissioning, and finally

measurement and verification. This process is outlined in Figure 1.

INTRODUCTION TO PERFORMANCE CONTRACTING

Figure 1: Performance Contract Process Outline


What percentage of a facility’s monthly and annual cost is the result of peak demand

charges versus electric consumption charges? How does this compare to other facilities

in the project portfolio? If the percentage is high, effective demand reduction or demand

shedding strategies should be investigated for feasability during of the site walkthroughs.

Electric Cost Breakdown

Although it is often rushed, or simply not completed by many companies, data analysis prior to a

site walkthrough is one of the most important stages of an effective performance contract at CMTA.

Analyzing the appropriate facility data before an initial walkthrough will lead to a more productive

site visit and more educated project decisions. At this project stage, specific equipment trends are

typically not accessible, but historical facility utility bills usually are. One can utilize this historical

utility data to benchmark the facilities and get acquainted with the site’s utility trends before a

walkthrough. Benchmarking and historical trend analysis can predict the existing system types

and corresponding potential issues throughout a project. It also provides valuable information on

which facilities have the most savings potential and shines light onto specific trends that need to

be investigated to maximize long-term project performance. The results of effective historical data

trending before a site visit allows CMTA to appropriately allocate the engineering team’s time and

efforts, yielding a better project for our clients.

As an example, analyzing a facility’s monthly electric demand (peak demand or monthly kW charges)

trends can produce very valuable information. Key trends to consider include:

EFFECTIVE DATA TRENDING FOR A PRODUCTIVE INITIAL WALKTHROUGH

Does the facility have a higher average peak demand per SF profile than other facilities

in the project portfolio? If so, what is the root cause of this trend? Is it the building’s

systems or is it how the systems are operated? Understanding this trend, and focusing the

engineering team’s time on it, will positively inform future project decisions.

Average Peak Demand Per Square Foot (SF) Benchmark

Are there particular months, or times of the year, peak demand increases above the annual

average in a way that can’t be explained by typical usage or weather profiles? If so, this may

indicate out of control systems or other issues that should be investigated during the site

walkthroughs.

Peak Demand Outliers


Another key electric demand related trend that should always be analyzed is “billed

demand” versus “actual demand”. Monitoring the billed demand trend before the initial

walkthrough will reveal facilities that are charged demand penalties. A demand penalty

is a charge in which a minimum demand level is billed regardless of the actual, metered

demand. A demand penalty may be a result of several factors (previous 11-month peak,

contract capacity, etc.) and would result in a larger utility bill than required on low demand

months as shown in Figure 2 below. Reducing, or potentially eliminating, excess billed

demand charges is an excellent opportunity for utility cost savings within a project.

If a facility is identified as having potential demand penalty savings opportunities before

the initial walkthrough, the walkthrough should be utilized to determine what is setting

the minimum demand charge or if the demand penalty is something that may be addressed

with the project. This will allow for more accurate savings estimates and expedited Energy

Conservation Measure (ECM) design. Not properly analyzing, and understanding, how

billed demand penalties affect a facility’s actual utility costs can have devastating results

on a performance contract. Potential cost savings opportunities will be missed and,

more importantly, the actual electric demand dollar savings can be negated by the “billed

demand” charges. This results in the project guaranteed demand savings not yielding the

dollars savings needed by the owner to properly fund the project. It is analyses like this, and

the emphasis that CMTA places on them, that allows our project scope, and its resulting

performance, to be unmatched.

Demand Penalties

Figure 2: Facility with Demand Penalties


Electric demand trends aren’t the only trends to look out for when benchmarking a facility. Other

key items that should, at a minimum, be investigated prior to the initial walkthrough include:

1. Appropriate seasonal energy variation (or lack thereof) for the facility type

2. The electric consumption to demand ratio for the facility type, also know as equivalent full load

hour (EFLH) analysis. EFLHs are calculated by taking energy usage (kWh) and dividing it by peak

demand (kW). High EFLHs indicate a facility that is running out of control (i.e. HVAC systems

not set back or lights, or other systems, that seldomly turned off).

3. Energy Usage Intensity (EUI)

When developing the project scope of work, the engineering team typically has access to the

existing building controls. Depending on the sophistication of the existing building control systems,

it can allow for access to extremely valuable equipment data trends. In addition to analyzing the

building controls data, effective data logging should be completed prior to final scope definition.

This includes logging of space temperatures, key equipment run hours, and key equipment electric

consumption as well as many other parameters. Using effective data logging techniques to analyze

system power consumption and runtimes can be extremely valuable in truly understanding the

actual life cycle benefits of various approaches and selecting the approach that best benefits the

project.

Effective analysis of equipment trends can reveal a variety of different ECM approaches and guide

project design decisions. Proper analysis of this data can result in valuable maintenance ECMs, night

and weekend setback scheduling ECMs, or other approaches that are often overlooked if the data

analysis steps are not properly executed. They can also indicate existing high energy consuming

designs that will require additional equipment, or reworked operation, to address.

For example, a VFD may already be installed on an Air Handling Unit (AHU) Fan, but the trends

may show that it is constantly running at 100% capacity. This could be indicative of a number of

issues, but the most common cause is an air blockage or leak in the distribution system. If the cause

is not an air leak, which is relatively simple to locate by inspection, it is likely the result of an air

obstruction in the system, such as a clogged filter or air blender in the AHU. If an AHU is controlled

to a duct static pressure setpoint, and the system contains a clogged outside air and return air

blender (or filter), that setpoint may not be able to be achieved. Therefore, the VFD would be driving

the AHU fan at 100% power constantly to try to achieve the static pressure setpoint.

A simple air blender (or filter) cleaning exercise would correct the issue and, due to the fan affinity

laws, result in substantial savings as the VFD operation is corrected. Figure 3 illustrates how the

VFD power could be affected by the air blender (or filter) cleaning. All exposed areas in red represent

DATA TRENDS TO DETERMINE SCOPE OF WORK


the energy savings that are achieved with this exercise. The resulting ECM would be to install an

access door in the AHU to facilitate future cleanings. Not only would this modification ensure the

sustainability of the energy savings, but it would substantially improve the comfort of the spaces

and make the facility maintenance personnel’s life easier. It is truly a win-win.

Figure 3: AHU VFD Power Before and After Air Blender Cleaning

Another example of where effective data analysis can be beneficial during the project design phase is

a facility containing a cooling tower where condensing water temperature control is accomplished

via a constant volume condensing water pump, tower bypass loop, and constant speed cooling tower

fan. The pump may have surplus pumping capacity, resulting in the installation of a triple duty valve

to throttle the pump flow back to the desired system flow. In this scenario, the pump and cooling

tower fan are both controlled at constant speed and a three-way valve on the bypass loop is used to

control to the desired condensing water loop temperature. A depiction of this system is featured in

Figure 4 on the following page.


Due to the system design, the condensing pump and cooling tower fan will run at full capacity

regardless of building load. Therefore, this system consistently wastes energy, particularly when

the full cooling capacity of the system is not required to maintain satisfactory indoor air conditions.

Therefore, this existing system provides a good opportunity to garner significant energy savings.

However, the specific approach to maximize the payback of the solution can only be properly

determined with the data logging methods described previously. For example, both the condensing

water pump and cooling tower fan can be converted to variable speed (using VFDs) to properly set

flow and condensing water temperature setpoints, or only one of the two pieces of equipment can be

converted. The final ECM scope is determined by evaluating which approach, if any, makes economic

sense in terms of payback for the project. This decision is driven by multiple factors such as cooling

tower, pump, fan and chiller sizing, and if the existing pumps and motors are VFD rated.

Equipment trends and data logging may also reveal that certain equipment runs 24/7. This is where

a night and weekend setback scheduling ECM could be applicable. The equipment setpoints would

essentially be loosened at unoccupied times to reduce the overall running time of the equipment.

This prolongs the life of the equipment while also saving on equipment maintenance costs, all

with operation changes that are only active when no one is in the building to feel any deviation in

environment. Another win-win.

Figure 4: Existing Condenser Water Loop Configuration


It is analyses like these that deliver ECMs that not only save energy and utility costs for our clients,

but also improve indoor air conditions, prolong the life of equipment, and improve the execution of

the building maintenance personnel’s everyday job.

Once new designs have been implemented in a facility, the next step is ensuring the new system

is operating per design intent. If the controls sequence is not implemented properly, issues will be

present in the programming, keeping the new system from operating at optimal performance. With

modern building control systems, or with the data logging techniques described previously, these

issues can be identified by monitoring the equipment data trends throughout the system. The result

of identifying issues early, through a data driven commissioning process, are undeniable. Effectively

tracking items such as fan run times, space temperature setpoints, discharge air temperatures, and

fan speeds during the commissioning process will result in increased occupant comfort and optimal

building performance.

In addition to utilizing data in the initial commissioning of the systems, effective data trending

and analysis should be used in a continuous commissioning approach to ensure building systems

continue to operate at peak performance over time. Take, for example, the 100% VFD speed fan on

the AHU ECM discussed previously. Trends and alarms can be set up to ensure this type of issue, or

other issues related to excessive VFD percentage, don’t reoccur over time and, if they do, the alarms

promptly alert staff to the situation. This allows the building staff to address the issue before excess

energy is wasted and building occupant complaints increase. A simple trend, such as accumulating

run hours at various VFD speed groups can be used to alarm the system and alert building operators

if the VFD runs more than expected, enabling building operators to correct the root issue. This is

just one of many ways to use effective data trending and analysis to ensure the sustainability of

energy savings in a performance contract, yielding top tier project performance.

Effective data trending and continuous commissioning may reveal issues that prevent select

systems from not only being optimally controlled but also properly functioning. For example, boilers

require a minimum flow in order to operate. If the minimum flow is not achieved in the building

hot water loop, the boiler safety will trip on low flow and become nonfunctional until it is reset.

However, once it is reset, it will then operate throughout the day as intended. If the building pumps

are not commanded on before the boilers at daily startup, this particular situation would occur.

While this may not result in excessive energy consumption during operation, it would result in

unnecessary work for the building operators and comfort issues throughout the building since the

heating required by the building would not be provided until the boiler is reset. An event like this

could be diagnosed by comparing the boiler error trend to the pump command trend. If the pump

command is not enabled before the boiler error occurs as shown in Figure 5, this is the root cause of

the boiler tripping issue, and the programming must be corrected. These continuous commissioning

USING TREND DATA DURING COMMISSIONING


trend analyses allow the ECMs implemented to remain sustainable and allow projects to yield top

tier performance year after year.

Figure 5: Boiler vs. Pump Command

Measurement and Verification (M&V) should inherently be data trending. When done at its highest

level and with a client-focused approach, M&V should simply be the process of tracking post-

construction utility bills to ensure savings detailed in the contract are indeed achieved in reality.

Analyzing the project results at the end of the savings guarantee period will reveal if the savings

were achieved. However, trending the data as soon as it is available in can effectively result in

sustainable savings.

For example, during the commissioning phase of a project, the building could be operating as

intended and savings could be tracking as needed to achieve the contract guarantee. However,

equipment has the capability to malfunction, or be overridden by building operators, after the

EFFECTIVELY TRENDING MEASUREMENT AND VERIFICATION DATA


project is complete and commissioned. Effectively trending Measurement and Verification data can

help bring these malfunctions, overrides, or other anomalies, to light and allow for an expedited

solution to return the building systems to optimal performance.

An example of telling trend data can be illustrated by comparing monthly gas consumption within

the same year. If there is a sudden, unexpected increase in gas consumption between months,

it could indicate the presence of failed equipment. For example, Figure 6 shows the monthly

gas consumption at a given facility. One may observe that there is an unexpected increase in

consumption during the month of October. This was due to a broken water heater relief valve that

was causing excessive gas consumption. With effective post project data trending, this issue was

caught and fixed right away. This caused the gas consumption trend to continue on its expected

trajectory for the remainder of the year and even continue on to provide excess savings beyond the

guarantee on the project. However, if the issue was not addressed, the projected gas consumption

with an unfixed relief valve would have surpassed the guaranteed consumption and the associated

guaranteed energy savings would not have been achieved. This type of trending (tracking actual

utility bills) is relevant and important to all utility savings included in the project guarantee.

Figure 6: Gas Consumption Increase with Faulty Relief Valve


Telling trend data can also be buried in the component-level trends post-construction. For example,

kitchen makeup air units are sometimes controlled in accordance with an exhaust fan’s operation.

If the exhaust fan is on, the makeup air unit is on to supply the air required as the exhaust fan

is removing it from the building. If the exhaust fan is off, so is the makeup air unit supply fan.

This will maintain building pressure. While the unit may be functioning as designed during the

commissioning phase of a project, over time the controls may be overridden to have the makeup

air unit supply fan constantly run at 100% power, regardless of the exhaust fan’s state. Figure 7

illustrates what this trend would look like for a supply fan in this situation. The solution to this is

simple – release the makeup air unit supply fan override, allowing it to return to its commissioned

operation sequence. Finding this override and releasing it was a no-cost solution that saved the

client thousands of dollars annually.

Figure 7: Makeup Air Unit Supply Fan Power

Actively tracking and acting on these types of utility and component-level trends allows

performance contracts to not only meet their guarantee but actually yield excess dollar savings on

the utility bills. These excess savings go straight to our client’s bottom line.


Analyzing the right data trends has the ability to enhance all phases of a performance contract.

Whether it’s reviewing the historical utility bills to determine the focus of an initial walkthrough or

analyzing the equipment level trend data to hone in on the most effective scope of work, utilizing the

available data to drive decisions is critical to an effective guaranteed energy savings performance

contract. The advantage doesn’t stop at benchmarking and design, it continues through all project

phases as the same effective data driven approaches are applied to commissioning and long-term

project measurement and verification (M&V) processes. While the most effective data to trend varies

as a project progresses, an effective performance contract begins and ends with analyzing the actual

facility performance through the metered utility bill data. If the data is monitored and utilized to

drive decisions throughout the entire life of a performance contract, the result is simply top tier

performance.

CONCLUSION

Katie H. Lacy, PE, LEED AP

Mrs. Lacy joined CMTA in 2017 as part of the Energy Solutions group and has been

exclusively working on Energy Savings Performance Contracts (ESPC) since. She has

intimate knowledge of all phases of an ESPC project for State, College, and K-12 Facilities

from conception, design, contract development, construction, and Measurement and

Verification. She is an expert in Measurement and Verification and has executed this role

on multiple ESPCs. She has a reputation of providing excellent service to her clients by

providing clear, detailed data analysis no matter what phase of the ESPC she is working on,

and is passionate about sharing her knowledge and expertise by mentoring and developing

younger engineers.

About the Author

utilizing data to maximize performance contract results

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