VET4SBO Level 1 module 4 - unit 2 - v0.9 en

ECVET Training for Operatorsof IoT-enabledSmart Buildings (VET4SBO)
2018-1-RS01-KA202-000411
Level 1
Module 4: Basics for the operation of smart building
using IoTs to improve the flexibility and the intelligence
in satisfying human comfort and/or energy efficiency
Unit 4.2: Quality-of-service criteria (occupants’ comfort,
energy efficiency, operational cost, security, etc.)

Outline
1. Quality of Service (QoS) meaning
2. Indicative list of QoS criteria in building monitoring and
control
– Cost effectiveness, energy efficiency, occupants’ comfort, security,
speed of performance, etc.
3. Basics of mathematical modelling of QoS criteria in the
building automation domain
4. Multi-objective building operation scenarios - combinationof
QoS criteria

Outline
1. Quality of Service (QoS) meaning
2. Indicative list of QoS criteria in building monitoring and
control
– Cost effectiveness , energy efficiency, occupants’ comfort, security,
speed of performance, etc.
3. Basics of mathematical modelling of QoS criteria in the
building automation domain
4. Multi-objective building operation scenarios - combinationof
QoS criteria

Quality of Service (QoS) meaning
“The degree to which a provided activity promotes customer
satisfaction. For example, quality of service (QoS) technologies
used in the electronic or telephone networking business typically
assist in optimizing network traffic management in order to
improve the experienceof network users.”
[Source] http://www.businessdictionary.com/definition/quality-of-service-QoS.html

Quality of service (QoS) is a measurable degree, at which a “system” achieves
its pre-defined service design objectives and/or performance indicators.
There are few important components in the above definition:
• The “system”. What the system is and what its intended service is?
• The Performance Indicators. What are the Key Performance Indicators
(KPIs) of the user/customer satisfaction?
• The metrics. How to measure the KPIs and subsequently the QoS?

Quality of service can be mapped in five dimensions:
1. reliability (ability to perform the service dependably, accurately, and
consistently)
2. responsiveness (providing prompt service)
3. assurance (ability to convey trust)
4. intelligence (individualised attention of needs) and
5. tangibles (physical evidence of the service).

QoS criteria in building monitoring and control
Indicative list of KPIs:
• KPI-1: Human/Occupants’ comfort
– Health protection, living/working efficiency, space utilisation and
flexibility, response speed, etc.
• KPI-2: Environmental friendliness – Energy efficiency
• KPI-3: Cost effectiveness – operation and maintenance with emphasis on
effectiveness
• KPI-4: Safety and security – e.g. measures against illegal entry, fire,
earthquake, disaster and structural damages, etc.
• KPI-5: Sustainability of other KPIs

KPI-1: Human/Occupants comfort
Building occupants comfort is linked to a range of parameters that are activated or not, based on the type of
the building and the application.
A key parameter is the ability of the building monitoring and control solution to maintain high air quality and
overall conditions in the building, so as not to put occupants’ health into any risk. The productivity is another
comfort parameter, since it is directly linked to the level of the support people receive from their building.
Productivity is not defined only in working environments; residential buildings, schools, etc, need their
occupants to be productive in the sense of achieving their everyday objectives as easily and quickly as
possible. This is linked also to the smartness in the utilisation of the building space and the flexibility provided
by the automation solutions.
Finally, occupants’ comfort is directly linked with the degree at which each of the automation sub-systems
achieves its design objectives. Here we assume that the occupants’ preferences are taken into consideration
in the design objectives of each sub-system, e.g. the lighting system delivers light when and where needed,
the HVAC system maintains comfortable condition of the building air, etc.

KPI-1: Human/Occupants comfort
A study by Lawrence Berkeley National Laboratory (LBNL) found that productivity went up 1 percent for
every 10-percent increase in air quality satisfaction. Οffice performance also increased due to better
temperature control, superior ventilation and reduced air pollutants, according to other research
initiatives of the same organisation.
A study published in the June 2016 issue of Environmental Health Perspectives found that a reduction
in volatile organic compound and carbon dioxide exposures, combined with better ventilation, resulted
in an improvement in cognitive scores of 61 percent. In particular, participants in the study had
significant increases in strategy, crisis response and information usage when moved to buildings that
meet green criteria for air quality.

KPI-2: Environmental friendliness – Energy efficiency
Building operators need to track energy usage, consumption habits and related
data as an effective environmental and cost-saving measure. Utility companies
already use smart devices to track energy consumption, with very high cost savings.
Tracking water consumption, quality, pressure, temperature and other data also
lets building operators quickly identify trends or potential problems and make any
necessary adjustments. Smart water leak detectors can identify a trickle before it
becomes a flood, without relying on excessive water consumption or visual
inspections to spot the problem. Smart meters can also detect a leak in a gas line,
which could prevent a significant safety liability.

KPI-3: Cost effectiveness – operation and maintenance with emphasis on effectiveness
Most smart building data have financial implications. Some ramifications are obvious: Being
able to track utility usage can lead to simple cost saving measures through conservation. Smart
buildings also allow for dynamic power consumption based on peak energy times and
ventilation, and cooling optimization based on occupancy and load variations – all of which
lead to financial savings. Smart building metrics may also identify problems before they lead to
expensive outages or equipment failures.
Building operators may have more difficulty quantifying other financial consequences, but they
are no less important. For example, improving ventilation and temperature for building
occupants results in quantifiable increases in productivity. Researchers translate these
increases as financial gains, which demonstrates that a more comfortable building can save
thousands of Euros per year.

KPI-4: Safety and security
In a 2015 survey by Honeywell, 51 percent of building operators rated safety and
security over any other advantage offered by smart buildings. Smart buildings can
feature superior fire system and emergency communication control, and they also
offer health and life safety systems, along with the ability to monitor them.
Surveillance and intrusion tracking can also help building operators detect unusual
activity that could indicate a threat to personnel or information.

KPI-5: Sustainabilityof other KPIs
Further to the importance of measuring each of the aforementioned KPIs and ensuring
high performance of the building, even more important is to find ways to sustain the high
performance. Therefore, smart buildings need to implement measures to ensure
continuous cost savings, occupants’ comfort, security and safety and energy efficiency.
For instance, the HVAC system needs to be able to continuously record the occupants’
preference and respond intelligently and adaptively. The security system needs to be
scalable and updatable to address any new threats and be able to integrate any new
technology that becomes available. The water monitoring system needs to be able to
integrate novel algorithms for leakage detection. Same holds for the building air quality
monitoring system, etc.

Implementing appropriate KPI Metrics
By collecting the right information, building operators are able to make judgments
based on sound data instead of theoretical estimates or trial and error. Even facility
managers with older building automation systems can track data and manage
operations through retrofitting or upgrading.
Tracking building performance metrics can lead to thousands of EUR of savings
each month while creating additional safety and operational gains. These potential
gains will drive building owners to add smart building performance tracking in their
normal work activity within the next few years. They need to be knowledgeable,
competent and ready for that.

Mathematical modelling of QoS criteria
1. Occupants’ comfort
Occupants’ comfort is a very broad term as it captures several parameters of
comfort. Each one of the parameters requires a separate definition of the metrics
that would enable the building operators to measure the performance against the
KPI. As an example here we refer to “thermal comfort” defined as the level of
satisfaction of the occupants of the temperature in the building spaces.
𝜓1 = 1−
Κ 𝜄
𝑒
Κ 𝜄
𝑐

1. Occupants’comfort
Where:
𝐾 is the total of the time the heating/cooling automation has been operating
𝐾 ⊆ 𝐾 is the period of time during which the performance is evaluated
𝐾𝑖
𝑐
⊆ 𝐾 is the period of time during which a certain building zone 𝑖 was occupant
(assuming the building is split into 𝑛 zones, with i = 1,2, … , 𝑛), therefore making the
occupants' thermal comfort criterion applicable
𝐾𝑖
𝜀
⊆ 𝐾𝑖
𝑐
is the time period during which the zone 𝑖 temperature remained within the
comfort zone; without loss of generality, the latter is defined as the operation with zone 𝑖
temperatures (𝑥𝑖(𝑘)) deviating from the desired value (𝑟𝑖(𝑘)) by more than 𝜀, i.e.,
|𝑥𝑖 𝑘 − 𝑟𝑖(𝑘)| > 𝜀.

1. Occupants’ comfort
The previous mathematical formulation is based on the thermal comfort standards of
ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers),
where a metric of thermal comfort is defined as ``the occupied time within a defined
time period in which the environmental conditions in an occupied space remain inside
the comfort zone''. The ASHRAE defines also more complex thermal comfort models,
taking multiple variables and factors into account, which can be incorporated if an
application requires it.

2. Energy efficiency
The energy efficiency metric compares the consumed energy in a building zone
with a theoretical maximum accumulated energy consumption in that zone.
𝜓2 = 1−
𝑣𝑖(𝑘)
𝑣 𝑚𝑎𝑥
𝑘∈𝐾

2. Energy efficiency
Where, in addition to what defined earlier:
𝑣𝑖 𝑘 is the vector of energy inputs (e.g., heating/cooling, lighting, etc) in zone 𝑖
𝑣 𝑚𝑎𝑥 is a theoretical maximum energy consumption,acting as normalisation factor. It
can be taken from historical data of building energy consumptionbefore the
application of the new monitoring and control solution.
The energy consumptionis summed over the whole evaluation period of the
performance of the solution. To calculate the consumptionfor the whole building, this
needs to be summed over all zones, as well.

3. Cost effectiveness – operation and maintenance
Like with the case of occupants’ comfort, there are several cost parameters in the operation of
a building. As discussed earlier, the productivity of occupants can also be measured and added
to the cost savings (assuming improvement of productivity due to better comfort conditions).
These cost parameters need to be considered separately, since they depend on different
building dynamics. However, in all cases, the calculated cost needs to be compared to historical
values of the operation cost or to a theoretical maximum. We choose here to present the
formulation of the energy cost, since it is of the most important ones in the overall operational
cost.
𝜓3 = 1−
𝑐𝑖(𝑣𝑖(𝑘))
𝑐 𝑚𝑎𝑥
𝑘∈𝐾

3. Cost effectiveness– operationand maintenance
Where, in addition to what defined earlier:
𝑐𝑖 𝑣𝑖 𝑘 is a function vector representing the cost of producing the zone 𝑖 energy inputs.
It is noted that each device is associated with its own cost function, which allows to
calculate the cost of operation at every energy output level.
𝑐 𝑚𝑎𝑥 is either a historical maximum cost of energy in the zone or a theoretical value that
considers the maximum possible consumption of the devices.
Other costs can be represented with similar formulas.

4. Safety and security
To define a metric for the safety and security KPI, we first need to create a complete list of
events/incidents that can potentially happen in a building and which can be prevented or
detected early with the support of novel monitoring and control techniques. For each of these
types of incidents, we need to calculate an average cost for the building owner and the society
as a whole (it may include also casualties, medical and insurance costs, etc.). Moreover, since
most of these types of events do not happen regularly and thus their cost cannot be calculated
deterministically, we use weighted terms that consider the probability of each type of event to
happen. A simple cost model would then be:
𝜓4 = 1−
𝑝𝑗 ∗ 𝑒𝑗𝑗∈𝐽
𝑝𝑗 ∗ 𝐸𝑗𝑗∈𝐽

4. Safety and security
Where:
𝐽 is the set of identified types of security/safety related incidents that can happen in a building,
with 𝑗 being its iterator
𝑒𝑗 is the estimated average cost of the results of the incident 𝑗 should it occur
𝑝𝑗 is the probability of the occurrence of the incident 𝑗 , acting as a weighting factor
𝐸𝑗 is the estimated average cost of the incident 𝑗 , assuming the unavailability of the
prevention or the early detection mechanism.
The costs are summed over the whole set of pre-identified types of incidents. It is clarified that
we assume only one occurrence of an incident type.

5. Sustainability of the performance
This is an equally important metric, since any savings that apply only for a short
period of time, make the investment not worth it.
There is no need to define a separate formula to use for calculations. In any case,
the time parameter is directly or indirectly included in the formulations of the other
metrics.

5. Sustainability of the performance
The sustainability of the good performance of the solution can be confirmed if the
performance is calculated as high when the operation time 𝐾 becomes long and
subsequently the evaluation time 𝐾 ⊆ 𝐾 is also a long period.
It is important for the high performance to remain consistent in time, although
small fluctuations can be considered normal; there are many factors affecting the
operation of the monitoring and control systems.

6. Comparingdifferentconfigurationsof monitoringand controlsystems
Building operators are typically expected to choose which monitoring and control solution
to implement, in order to minimize the costs, considering the metrics defined earlier.
Assuming that you have data from the implementation of a number of possible solutions,
the “selection” operation would be formulated as:
𝑎𝑟𝑔𝑚𝑖𝑛 𝑓 𝐽(⋅) 𝜔𝑗 ∗ 𝜓𝑗(⋅)
𝑛 𝜓
𝑗=1

6. Comparingdifferentconfigurationsof monitoringand controlsystems
Where:
𝑓𝐽(⋅) defines the set of all solution implementations J, where the selection process iterates
with the iterator j.
𝜓𝑗 ⋅ , 𝑗 = 1,… , 𝑛 𝜓 define the pre-defined QoS criteria utilised for the selection process
(e.g., the ones discussed in our lesson here), and
𝜔𝑗, 𝑗 = 1, … , 𝑛 𝜓 define the weights assumed for each criterion. The weights take values in
the range [0,1] and model the significance given to each criterion by the building operator.

In practice, it is not reasonable to invest and implement many different monitoring
and control solutions, in order to choose the one with the best performance.
However, in some cases, different configuration options of the automation system
may be achieved by changing the way the components are used and/or by changing
certain parameters of the components.
For instance, combining measurements from one automation sub-system, in order
to implement functionality of other sub-systems is a way to achieve different
configurations, i.e. occupancy sensors can be used to detect malicious presence in a
room for the security system, as well as detect occupancy of room to trigger air
conditioning and/or lighting functions. Moreover, a cloud-enabled fault detection
algorithm can be instantiated for the detection of faults in multiple building zones.

The “selection” process could be implemented in the following ways:
• Choose one of the options randomly
• Associate the participating components with a rating mechanism, such as for the
components participating in “well-performing” configurations to have their
rating score increased by a certain value.
• A third option would be to test each configuration option for a certain amount of
time on the real system and choose the one with higher performance against the
pre-defined QoS criteria.
• A fourth option would be to test each configuration in a simulation, using pre-
defined models for the building and the components.

Each option has its own advantages and drawbacks; the first two fail to
provide high confidence about the real system operation and performance,
while the third requires that untested configuration options are put in
operation, with unpredicted behaviour. The fourth option makes the
assumption that the installation is accompanied by test-models of the
components. These are not trivial to obtain, unless Architects start to export
this information as part of their building designs.

The method that provides a good balance between possibility of
implementation and confidence of result, is the one considering the
simulations. There are new software platforms being implemented, which
allow running simulations to measure the performance of the monitoring and
control configurations, considering the so-called “digital twin” of the actual
system (the building) [1].
[1] http://www.domognostics.eu/

Therefore, using such solutions each of the valid configuration options is passed
through a fixed-time simulation test and is evaluated against the QoS criteria. The
simulation test uses a “digital twin” instantiated with certain parameters so as to
offer evaluation on equal basis.
“Digital Twin”: it is an ICT system mirroring, shadowing and threading its physical
twin. Such systems recently become active and extend the characteristics of their
physical counterpart. They can also take action on behalf of their physical
counterpart, becoming a sort of avatar. [1]
[1] https://cmte.ieee.org/futuredirections/2019/07/07/digital-twins-where-we-are-where-we-go-vii/

Multi-objective scenarios - QoS criteria
combinations
We have seen that the performance of a building monitoring and control
solution does not depend on a single KPI. There are multiple KPIs defined,
which can be broken down into more detailed KPIs.
We have also seen that the selection of which KPIs to apply, depends on the
actual needs of the subject building, its occupants, its owners, its
functionality (e.g. education, business offices, residential, government, etc.).

combinations
We have also seen that not all KPIs need to have the same significance across
buildings and applications. In case we want to evaluate the performance
against multiple KPIs, we need to first define how important each KPI is for
our specifications.
Therefore,combining multiple monitoring and control objectives, and
subsequently combining multiple QoS performance criteria metrics, requires
a thorough understanding of possibilities and potential implications.

combinations
When combining multiple metrics’ formulas in a multi-objective optimisation
problem, while the optimization variables can take continuous values, the
solution may be a “pareto front”.
A pareto front is a set of non-dominated solutions, being chosen as optimal,
if no objective can be improved without sacrificing at least one other
objective. On the other hand a solution x* is referred to as dominated by
another solution x if, and only if, x is equally good or better than x* with
respect to all objectives. [3]
[3] https://www.igi-global.com/dictionary/elitist-mutated-multi-objective-particle-swarm-optimization-
for-engineering-design/21878

combinations
In other words, depending on the metrics chosen and the criteria generating the
multiple objectives, there can be more than one solutions for a system parameter
that can be configured to take a range of values. For instance, the specified heating
performance may be achievable with one heating device in ON state working at a
high energy consumption level or with two heating devices in ON state each one
working at lower consumption levels. In these cases, the building operator may
either choose one for them randomly, or define more criteria that would allow one
solution to dominate over the other, or even new solutions to come into play.

combinations
Discussion on QoS criteria combination:
The primary challenge of building monitoring and control systems is the minimisation of energy
consumption, while occupant comfort level is maximised.
These two QoS criteria are generally conflicting to each other. Therefore, sustainable smart
buildings always need to effectively manage the energy consumption and comfort and attain
the trade-off between the two.
The cost effectiveness is a criterion that always gets into the equation, since it is directly linked
with the interests of the building owners. What differs among applications and scenarios is the
weight used next to the cost variable. For instance, in working environments, where the
personnel is the most valuable asset and the most expensive resource, maintaining
comfortable environment receives higher significance weight than reducing the cost of
operating and maintaining the building.

combinations
Discussion on QoS criteria combination:
It is also noted that energy savings are directly linked with operation cost reduction,
in addition to environment friendliness.
The security and safety criterion is in general a difficult one to measure, however,
sustainability of the building should always give a good weight to the security and
safety of occupants and the building assets themselves.
So it looks like the question is how to set the weights of the pre-defined criteria,
rather than to select few of them to apply in the calculations.

combinations
Scenario 1 – Minimization of energy costs, CO2 emissions (safety/security),
discomfort, and technical wear-out (maintenance costs)
Building appliances, as well as heating and air-conditioning devices are encoded in
two different ways so as to respect the optimisation of operating times ad the
optimisation of operating modes.
The proposed optimisation technique has been applied in two types of buildings: a
commercial and a residential. Obviously, the weights on the QoS criteria differ in
each type of building. That is, avoiding the discomfort of working personnel may be
more important in the commercial building than it is in a residential building where,
e.g. occupants may sacrifice their thermal comfort in some time-windows in favour
of a lower electricity utility bill.

combinations
Scenario 1 – Minimization of energy costs, CO2 emissions (safety/security), discomfort, and
technical wear-out (maintenance costs)
Appliances comprise the dominant asset in a residential building, therefore having a solution
that schedules the operation, e.g. of a washing machine, considering the dynamic electricity
prices or having a solution that changes the operation temperature of the refrigerator based on
the measured room temperature, would definitely save money and at the same time reduce
gas emissions and devices wear-out.
On the other hand, the operator of the commercial building may consider a solution that will
operate heating and cooling based on zones occupancy measurement, time of day/night,
schedule of appointments and even individual preferences of personnel. This will minimise the
energy costs (subsequently gas emissions and wear-out), without compromising the comfort of
working personnel.

combinations
Scenario2 – Minimise energyconsumptionand maximise occupantscomfortin a school
Schools usually operate on a tight budget and any savings contribute a lot to improving the
actual education level and education resources available to children/students and let
everybody concentrate to their primary mission: education.
A building operator in a school, would therefore strive to optimise the HVACperformance,
considering off-time use, required comfort conditions in classes, etc.
The first step is to install IoT temperature, humidity and air quality sensors in indicative
zones of the school and monitor the air condition properties. If the result can be
justified by the operation of the HVAC system, then they can move on looking for
means to optimise the performance.

combinations
Scenario 2 – Minimise energy consumption and maximise occupants comfort in a school
On the other hand, if it appears that the HVAC system does not produce the expected results, then
the solution needs to also consider adding IoT actuators to support the HVAC system and/or over-
riding the setpoint algorithm with another algorithm that takes into account the new IoT
measurements.
With the right equipment, the school will be able to reduce off-hoursenergy consumption,
optimise the usage of the equipment, and create comfortable conditions for everyone in the school
(or at least maintain the conditions within specified limits).
In more details, the school can use pre-set thermostat schedules to regulate the classroom
environment and data analytics to make smart decisions on what/when/why to operate and also
detect any hidden faults quickly to get remedial actions.

combinations
Scenario 3 – Performance of buildings managed by a Commercial Real
Estate company
The real-estate company was undertaking the management of buildings, and billing utility costs
on a per-square-meter basis. As a result, clients who did not operate excessive hours were
paying for a portion of the clients consuming a lot of energy.
The company decides to apply IoT solutions to understand the real operation of the buildings
and apply fairer cost models. In addition, there have been some floods in certain buildings in
last two years, which resulted in high spending and had to be prevented.
They decided to use low-cost IoT sensors: water leakage detection sensors in bathrooms and
near water coolers, a sensor in the parking garages, and valves on main water lines that could
be shut off if the applied algorithms suggested high probability for a flood.

combinations
Scenario 3 – Performance of buildings managed by a Commercial Real
Estate company
The solution resulted in a fair and balanced billing for the company’s clients with respect to the
electricity bills, in addition to over 22% in energy savings.
Beyond the direct savings in individual buildings, the real-estate management company can
enjoy multiplied return on the investment due to applying same solutions across buildings, not
to mention the amounts of data they collect and which allow for additional analytics to make
smarter building management decisions.
This example shows that the operation and maintenance costs have a great variety of
dimensions across businesses, across buildings, and leave much space for creative solutions
and cost optimisations.

combinations
Scenario 4 – Energy savings Vs occupants’ safety in a shopping mall
A shopping mall had a heating and cooling system installed, however, individual
shop owners had access to outside-facing windows and used them day and night to
let fresh air in the space. This resulted in very high energy costs for the mall owning
company.
The building operator made the suggestion to install a smart HVAC system, cancel
all windows and manage the inside air conditions automatically, mixing fresh air
with re-cycled air at accepted levels.

combinations
Scenario 4 – Energy savings Vs occupants’ safety in a shopping mall
However, the building owner was worrying about possible air contamination
incidents, where accidentally or maliciously the air would be polluted. This
has a low possibility, but should it occur it destroys the business.
The building operator reconsidered the criteria and suggested to deploy also
low-cost IoT sensors measuring air quality parameters like CO, CO2 and
several particulate matters. The investment was not that high and the
combination of the HVAC with the air quality measurements gave enough
safety confidence, allowing benefiting also from the lower energy
consumption.

combinations
Scenario 5 – Crowd-source-based criteria weighting
The operator of a large government building was tasked to exploit IoT technologies
to reduce the energy consumption of the building and subsequently the
operational costs.
After an initial study, the operator suggested the deployment of occupancy sensors
and window opening sensors, which guided the operation of the heating/cooling
systems.
In addition, the heating/cooling setpoints were being set automatically based on
the time-of-day and reported weather conditions. Since offices were operating
from 8:00 to 15:00, big amounts of energy were saved immediately.

combinations
Scenario5 – Crowd-source-basedcriteriaweighting
However, a percentage of government workers and people attending the various
departments to use government services, started complaining of uncomfortable thermal
conditions in the building, while others were congratulating the building operator for the
good conditions.
In order to balance the situation and attend to the needs of all occupants, the building
operator suggested to use a mobile app through which people could quickly express their
feeling in relation with the thermal conditions. An algorithm was analysing the collected
feedback in combination with the location of the person.
The system was making an overall decision about the setpoints in each building zone, so as
to maximise the comfort levels.

combinations
Scenario 6 – Security Vs operational cost in an airport
An airport operating company, decided to deploy a system with smart cameras in
all common spaces, which formed a closed surveillance circuit. The data were
transferred directly in a cloud service which run smart analysis algorithms for the
detection of suspicious behaviour to support decision making.
After two years of operation, the company realised that the cost of maintaining the
high bandwidth data channels and the intermediate storage equipment for the
amount of data collected, became unevenly high.

combinations
Scenario 6 – Security Vs operational cost in an airport
They hired a new building operator who suggested replacing the cameras with IoT-
enabled ones, which also had the capacity to run event detection algorithms on
their local boards. This way, the cameras were doing the analysis on the collected
data and exchanged data only with neighbouring nodes.
They only sent small packages of data to the cloud service if something suspicious
had already been detected, for further centralised analysis and higher-level
decision making.
The solution achieved at least same level of security, while also minimizing the
operation cost.

combinations
Scenario 7 – Energy efficiency Vs productivity
A company which operated through a large commercial building, decided to
take measures to reduce the energy consumption.
The building operator informed them that the costs were mainly due to the
electricity consumption of heating/cooling and the lighting. She then
suggested to take some immediate measured to check the savings, by
modifying the temperaturesetpoints slightly; lower in winter, higher in
summer.
She also suggested to reduce the intensity of the light and cancel the
operation of water cooling and refrigerators in the kitchens.

combinations
Scenario 7 – Energy efficiency Vs productivity
The company saw an immediate reduction of utility bills, however the long
operation revealed a productivity issue which was threatening the revenue
levels.
The company did the calculations, which suggested that there was no benefit
from sacrificing productivity in favour of the energy cost reductions.
Therefore,they gave higher weight to the productivity criterion.
What else can they do to avoid compromising the energy savings they already
achieved?

Resources
• https://searchunifiedcommunications.techtarget.com/definition/Q
oS-Quality-of-Service
• https://www.techopedia.com/definition/9049/quality-of-service
• https://www.sciencedirect.com/topics/engineering/intelligent-
buildings
• https://blog.senseware.co/2017/04/17/5-essential-smart-building-
metrics-measure
• https://www.facilitiesnet.com/buildingautomation/tip/Project-
Goals-KPIs-Shape-Intelligent-Buildings--36445

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.

VET4SBO Level 1 module 4 - unit 2 - v0.9 en

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VET4SBO Level 1 module 4 - unit 2 - v0.9 en