This document discusses the costs and cost savings associated with implementing Internet of Things (IoT) devices in building management systems. Traditional building management systems are costly to deploy, with average costs of $2.30 per square foot, limiting their use primarily to large buildings. New IoT devices provide a more cost effective solution, costing $5,000-$50,000 to deploy in a building compared to $250,000 for a traditional system in a 100,000 square foot building. IoT devices can save 10-25% on energy costs annually, with payback periods of 6 months to 2 years. Widespread deployment of IoT for building management can provide significant operational cost savings across portfolios of buildings.

1. ECVET Training for Operatorsof IoT-enabledSmart Buildings (VET4SBO)
2018-1-RS01-KA202-000411
Level: 3 (three)
Module: 1 Interdependencies of building operation
sub-systems
Unit 1.4 Costs analysis

2. L3-M1-U1.4 Costs analysis
• A fundamental breaktrough is occurring in building controls,
driven by a new generation of powerful Internet of Things
(IoT) devices.
• These networked sensors and controls can be quickly and
cost-effectively deployed in commercial buildings and
industrial sites to generate large amounts of valuable data
for analytics and automation.

3. L3-M1-U1.4 Costs analysis
• Building managers benefit from this granular new data and
control capability with the ability to fine-tune processes at a
greater number of building systems to lower energy and
maintenance costs.
• The resulting savings can provide compelling return on
investment (ROI), even for buildings under 100,000 square
feet.

4. L3-M1-U1.4 Costs analysis
• Building management systems (BMS) or building automation systems
(BAS) are the traditional solution to addressing the problem of energy
waste.
• The average cost to deploy a basic BMS is about $2.30 per square
foot, equivalent to $250,000 for a 100,000 square foot building.
• This cost means low ROI is a challenge, which limits most BMS
deployments to the major subsystems, such as HVAC and lighting in
high-traffic areas, and of only the largest buildings, over 100,000
square feet.

5. L3-M1-U1.4 Costs analysis
• BMS is rarely deployed into buildings under 100,000 square
feet, which constitute about 90% of the total building stock in
the U.S.
• Even in the 10% of larger buildings, BMS often isn’t used in
low-traffic areas such as warehouses, stockrooms, or garages,
or to distributed equipment such as pumps, generators, or
parking lot lights on campuses and industrialsites.
• Hundreds of millions of square feet of real estate and millions
of remote equipment assets are not monitored or managed at
all for energy or operational savings.

6. L3-M1-U1.4 Costs analysis
• Solutions are now arriving in the form of IoT-generation
connected devices.
• Advancements in sensor and controls technology now enable a
new wave of advanced, non-invasive, cost-effective, and quick-
to-install products.
• Because properly-deployed and connected IoT products can
overcome the capital barriers of installing traditional BMS, a
vanguard applicationfor these devices is for energy
management in buildings and remote equipment.

7. L3-M1-U1.4 Costs analysis
• For the first time, these new products can be cost-effectively
deployed by non-specialized personnel and extend the reach of
existing BMS systems,
• or even begin to replace BMS in mainstream applicationsin
under-100,000 square foot buildings.
• The “big data” these new IoT devices generate can be gathered
into cloud-basedmanagement and analytics services via
existing networks, and the devices can be easily monitored and
controlled by facilities managers via smartphones and tablets.

8. L3-M1-U1.4 Costs analysis
• Focusing on HVAC, lighting, and some types of
electrical loads, it is reasonable to expect savings in
the range of 10% to 25% when implementing proactive
energy management programs in mid-sized buildings.
Source:
https://www.iotone.com/

9. L3-M1-U1.4 Costs analysis
• Adding IoT-based controls and monitoring to a building can
cost from just $5,000 to $50,000, which is a fraction of
traditional BMS costs.
• The process typically requires a systems integrator or in-house
electrician and IT network professional.
• An energy engineering specialist is recommended to analyze
the data and make recommendations on process optimization
and automation in order to maximize savings.

10. L3-M1-U1.4 Costs analysis
• Focusing on HVAC, lighting, and some types of electrical loads, it is reasonable
to expect savings in the range of 10% to 25% when implementing proactive
energy management programs in mid-sized buildings.
• For a typical 75,000 square foot building, this equates to an annual potential
savings of $15,000 to $50,000 per year.
• Some buildings can save over $100,000 annually, and ROI can occur in six months
to two years. Beyond the pure monetary savings, additional benefits related to
sustainability and environmental stewardship can also be realized, with detailed
data to support them.

11. L3-M1-U1.4 Costs analysis
Solving the interoperability challenges
• Perhaps the biggest challenge to providing cost-effective, high-ROI energy
management to mid-sized buildings has been the lack of interoperability
between devices.
• The interoperability problem is compounded by the need to interconnect both
legacy equipment in the buildings
• There are dozens of protocol standards and literally hundreds of different
implementations just among the best-in-class of these devices.

12. L3-M1-U1.4 Costs analysis
Data Are Expensive to Acquire & Utilize
• The cost of sensors has dropped precipitously in recent years. In 2004,the
average cost per sensor was $1.30.In 2020,the average cost per sensor is
expected to be $0.38.Unfortunately, the cost reductions in sensors have not
resulted in a significant decrease in the cost of a full BMS installation.
• As of 2014,the cost to deploy a basic BMS was at least $2.50per square foot
and could be as high as $7.00 per square foot. While the cost of sensors has
plummeted, the cost of equipment controls has remained stubbornly high.

13. L3-M1-U1.4 Costs analysis
There May be Limited Value for Data
• Datahave little value on its own. A data set is only as good as the insights that
can be derived.
• In the case of BMS data, insights usually involve equipment schedules, set
points, and systemconfiguration optimizations.
• For example, by identifying that the HVAC system is running when the
building is unoccupied, a building can make significant reductions to
operating expenses through utility consumption.

14. L3-M1-U1.4 Costs analysis
• Likewise, highly granular data sets around startup and shutdown processes may yield
optimization insights for system configuration.
• There are plenty of examples of poorly configured building management systems that can
yield significant savings if optimized.
• However, this is much more likely to be the case in unique building types, such as hotels and
stadiums that have constantly varying occupancy rates and schedules. For office and
multifamily apartment buildings, which have relatively consistent schedules and occupancy
rates year-round, the BMS may already be close to optimized.
• While there is likely to be “performance drift” in any building type over time, the point is
that no amount of data will yield significant results if the system is already close to
optimized and rarely requires changes.

15. L3-M1-U1.4 Costs analysis
Scalability
• The final limitation of using BMS data to optimize a portfolio of buildings is the
inherent lack of scalability.
• Perhaps each building in the portfolio has the same BMS vendor, but that is highly
unlikely.
• Each vendor is going to have its own proprietary data protocol, which requires a
developing and maintaining a number of different processes and integrations.
• Not only is this hard to manage and maintain, but the BMS vendors often have
competing products and thus are incentivized to make their data inaccessible to
third parties.

16. L3-M1-U1.4 Costs analysis
• While part of the difficulty of extracting BMS data may be competitive, there is also
a legitimate concern about security.
• Building data is not only valuable to competitors, because it is tied to controls of
building equipment, it can be extremely dangerous to occupancy if manipulated.
• There is also the consideration that a portfolio will contain a mix of properties with
and without building management systems installed.
• Leveraging BMS data to find operational waste may be effective in portions of the
portfolio, but a separate solution will be required for the buildings without a BMS.
• This requires an additional round of diligence and technology evaluations that will
slow down rollout and stifle scalability. The point is, relying on BMS data to drive
operational improvements will necessarily run into scalability issues.

17. Resources
https://cdn2.hubspot.net/hubfs/612214/Case%20Studies%20an
d%20WPs/Defining_the_Growing_Market_for_AFDD_Whitepape
r.pdf

18. Thank you for your attention.
https://pixabay.com/illustrations/thank-you-polaroid-letters-2490552/

19. Disclaimer
For further information, relatedto the VET4SBO project, please visit the project’swebsite at https://smart-building-
operator.euor visit us at https://www.facebook.com/Vet4sbo.
Downloadour mobile app at https://play.google.com/store/apps/details?id=com.vet4sbo.mobile.
This project (2018-1-RS01-KA202-000411) has been funded with support from the European Commission (Erasmus+
Programme). Thispublicationreflects the views only of the author, and the Commission cannot be held responsible
for any use which may be made of the informationcontainedtherein.