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Demand-controlled energy systems in commercial and institutional buildings: a review of methods and potentials


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Heating, ventilation and airconditioning (HVAC) are by far the most energy intensive systems in commercial and institutional buildings with office spaces. This makes HVAC systems attractive targets for energy efficiency improvement. New technological advancements can play significant role on improving energy efficiency. Such advancements have been also emerged in form of novel management and control strategies, which might lead to considerable energy savings with relatively minor investments. This paper evaluates demand control HVAC and lighting to assess the energy saving potential of upgrading the conventional building energy systems. This paper provides a summary of different methods and occupancy detection technologies. A range of technologies and methods are covered that vary in complexity, limitations and energy saving potential. Additional benefits such as demand response are evaluated and other emerging applications are discussed. Based on the review of methods and potentials, the paper assesses the state of the art in demand controlled energy systems and suggests areas for further research.
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Demand-controlled energy systems in
commercial and institutional buildings:
a review of methods and potentials
Shoaib Azizi
Department of Applied Physics and Electronics
Umeå University
90187 Umeå
Gireesh Nair
Department of Applied Physics and Electronics
Umeå University
90187 Umeå
Thomas Olofsson
Department of Applied Physics and Electronics
Umeå University
90187 Umeå
occupancy detection, demand-side management
Heating, ventilation and air-conditioning (HVAC) are by far
the most energy intensive systems in commercial and institu-
tional buildings with oce spaces. is makes HVAC systems
attractive targets for energy eciency improvement. New tech-
nological advancements can play signicant role on improving
energy eciency. Such advancements have been also emerged
in form of novel management and control strategies, which
might lead to considerable energy savings with relatively minor
investments. is paper evaluates demand control HVAC and
lighting to assess the energy saving potential of upgrading the
conventional building energy systems.
is paper provides a summary of dierent methods and oc-
cupancy detection technologies. A range of technologies and
methods are covered that vary in complexity, limitations and
energy saving potential. Additional benets such as demand
response are evaluated and other emerging applications are
discussed. Based on the review of methods and potentials, the
paper assesses the state of the art in demand controlled energy
systems and suggests areas for further research.
Today, buildings are responsible for up to 40% of total nal
energy use in developed countries above industry or transport
sectors (U.N. 2009). Energy use in the building sector is grow-
ing due to population increase, rise in the time spent inside
buildings and improvement in building services and comfort
levels (Pérez-Lombard, Ortiz, and Pout 2008). HVAC systems
used for space conditioning to maintain thermal comfort and
ventilation requirements have become predominantly the most
important end user in the built environment in both residen-
tial and non-residential buildings (EIA2019). e increase in
energy use related to HVAC systems are predicted to continue
in non-residential buildings in the coming years (EIA2019).
Non-residential buildings accounted for the highest growing
rate in building sector in terms of energy use before the eco-
nomic crisis (Pérez-Lombard, Ortiz, and Pout 2008). During
the crisis, from 2007–12 the nal energy consumption in Eu-
rope decreased by 8% while on the contrary the nal energy
consumption of non-residential buildings remained quite sta-
ble (Saheb et al. 2015). Non-residential buildings account for
a quarter of total energy consumption in European building
stock and comprised of dierent typologies such as oces, ho-
tels and restaurant, educational, hospitals, wholesale and retail
(BPIE 2011). Dierent types of buildings have dierent distri-
bution of energy use although HVAC, followed by lighting are
the most energy intensive systems in European non-residential
buildings (Balaras et al. 2017). In Canada, they are responsible
for 66% of energy use in commercial and institutional build-
ings (NRCan 2015). Oce buildings are important typol-
ogy within non-residential building category and consume
between 2% and 3.2% of nal energy use (Pérez-Lombard,
Ortiz, and Pout2008). Since 2008 the electricity consumption
has increased by 74% mainly due to increase in required IT
devices, new telecommunication types, new appliances and
air-conditioning (DAgostino, Zangheri, and Castellazzi 2017).
Oce buildings comprise of the second biggest category of
non-residential oor spaces. ey have similar cooling and
8-227-19 AZIZI ET AL
heating conditions as residential buildings while they are used
in shorter periods (BPIE 2011).
Building energy systems are meant to provide a comfortable
environment for the occupants in the building. e level of oc-
cupancy has a great impact on building’s indoor environmental
qualities (IEQ) thus on energy use in the building (Elie and C.
2012). is is because occupants generate CO2, sensible and la-
tent heat and their behaviour such as opening windows and us-
ing equipment aect IEQ in the building. Demand-controlled
energy systems are meant to deliver the services only when
and where they are needed, in the amount that they are needed
(Shen, Newsham, and Gunay 2017). is strategy requires ac-
curate occupancy detection in real time. It is even obligatory in
some building codes and standards for certain building spaces
to have occupancy sensors for lighting control (IRC 2011).
Occupancy detection has long been practiced especially in
the eld of automated lighting systems (Guo et al. 2010) e
application of occupancy detection technologies in the HVAC
area is relatively recent and emerging eld (B. Dong and Lam
2011; Gunay, O&Amp, et al. 2016). Occupancy detection tech-
nologies provide information that can be used by building con-
trol systems to operate the energy systems proportional to the
number of occupants in the building. Accordingly, the energy
use would be optimized by integrated control of active and
passive heating, cooling, lighting and ventilation systems (Sun
et al. 2013). Webber et al. (2006) investigated the energy use
by oce equipment aer working hours and found the turn-
o rate of most types of equipment are under 50% signifying
considerable energy saving potential. A study in South Africa
showed higher energy use during non-working hours than
working hours in commercial buildings (Masoso and Grobler
2010). e occupant’s behaviour and poor building control are
presented as the reasons for such energy waste while occupan-
cy-based building automation systems can be a solution to im-
prove the energy eciency.
e objective of this paper is to review the conventional
and new innovative methods of detecting building occupancy.
Resolution and accuracy are dened as two important qualities
of occupancy detection. Conventional methods of occupancy
detection are identied and implicit sensors linked together in
network is introduced as important emerging approach. Sub-
sequently, energy eciency potential of demand control energy
systems are investigated and other possible applications are
presented. e nal section provides some concluding remarks
and presents areas for future research.
Qualities of occupancy detection
Dierent applications of occupancy detection require dier-
ent qualities of detection. Resolution and accuracy are distin-
guished the main qualities of occupancy detection in the litera-
ture (Mel et al. 2011).
Most common methods of occupancy detection are limited to
a binary output showing whether or not a space is occupied.
However, many applications require more detailed information
such as the number of people in the space. Mel et al. dene
resolution of a sensor as measure of the quality of information
that consists of 3dimensions (Figure1). As resolution of a sen-
sor increases the occupant becomes more dened, the space
becomes smaller and the information is available more quickly.
Four levels of occupant resolution include:
Level 1: Occupancy; whether there is at least one person
present in the zone
Level 2: Count; how many people are present in the zone
Level 3: Identity; who are the people present in the zone
Level 4: Activity; what are the present people occupied with
In order for the building management systems to operate ef-
ciently, the occupancy information has to be accurate and
reliable. e accuracy of occupancy detection can be dened
in two dierent ways based on whether considering the pres-
ence or absence of occupants (Gunay, Fuller, et al. 2016). e
accuracy of presence detection is the ratio of correct presence
detections and the accuracy of absence detection is the ratio of
correct absence detection. In order to calculate these ratios, the
acquired occupancy information is contrasted to the ground-
truth. e ground truth is obtained with a reliable method of
occupancy detection, e.g. in a study by Ghai et al. (2012) the
occupants manually tagged their location by using a desktop
application. Similarly, there are two types of occupancy detec-
tion errors (Shen, Newsham, and Gunay 2017). False negative
detection is when the zone is occupied but wrongly concluded
to be empty. On the other hand, false positive detection is when
a zone is empty but wrongly considered to be occupied.
False negative errors are more problematic type in the eld
of building energy management. For example, a false negative
error causes the automatic lighting systems to be switched o
when space is occupied leading to occupant’s dissatisfaction
and reduction in productivity. Occupants’ annoyance might
lead them to nd ways to resolve the problems by dismantling
the sensors or control systems leading to adverse energy per-
formance (O’Brien and Gunay 2014). Nagy et al. (2015) found,
even 90% accuracy of presence detection can cause considera-
ble dissatisfaction for the occupants in the case of lighting con-
trol. Nevertheless, the required accuracy might dier in dier-
ent applications. For example, occupancy detection for heating
control might require less accuracy because of the buildings’
thermal inertia. e concerns about false negatives usually lead
Figure 1. Resolution of occupancy detection (Melfi et al. 2011).
8-227-19 AZIZI ET AL
the designers to adjust long timeout period for occupancy sen-
sors (Nagy et al. 2015). e system must detect no occupancy
during the entire timeout period before it undertakes a control
action. is increases the condence that there is no occupancy
despite increase in false positive errors leading to lower energy
eciency potential. Increasing accuracy of occupancy detec-
tion usually associates with higher investment in deployment.
us, the decision on level of accuracy should be based on ac-
ceptable return on investment calculations that depends on the
application of occupancy detection system.
Technologies and Methods of sensing occupancy
PIR sensors are one the most common types of sensors that
are oen used in building automation applications specically
for automatic lighting systems (Guo et al. 2010). ese devices
detect movements by sensing the changes in infrared radia-
tion thus, inferring human presence in the space (Dodier et al.
2006). Since movement is the key determinant for functionality
of these sensors, presence detection is not possible during the
periods of immobility. Moreover, these sensors require direct
line of sight, thus, they cannot detect movements happening
behind the objects in a room (e.g. a partition). In order to de-
crease false negative errors, usually an arbitrary delay period
is introduced. e more being conservative on occupant an-
noyance and discomfort, the longer delay period is selected.
Long delay period reduces energy eciency potential, which
is the primary intention of these systems; thus, the optimum
delay period is required to be determined. Nagy et al. (2015)
examined the accuracy of presence detection with dierent set
delay periods. ey inferred that the optimum delay period for
lighting control is when the accuracy level for presence detec-
tion reaches 95%. e delay period associated with this accu-
racy level lies between 4 and 20min depends on the space and
location of installed sensors. Figure 2 by Gunay et al. (2016)
shows similar approach that can be used to develop algorithms
to determine the optimum delay periods for sensors in their
specic location of installation.
Considering the PIR sensor’s line of sight, their accuracy is
sensitive to where they are installed. Furniture and room ge-
ometry can sometimes disrupt motion detection therefore; it
might be more ecient to use multiple sensors to cover a room
(Tiller et al. 2009). ere are also other limitations to some PIR
sensors on where they can be installed. Some PIR sensors are
incorporated with other devices such as switches and thermo-
stats in order to reduce the costs (Shen, Newsham, and Gunay
2017). Switches are designed to be installed 1m above the oor
while thermostats are supposed to be installed 1.5m above the
oor. e PIR sensors that are built-in parts of these devices
are likely to have suboptimal performance due to positioning
limitations (Shen, Newsham, and Gunay 2017).
Ultrasonic sensors are a type of occupancy sensors that similar
to PIR sensors detect motions. Unlike PIR sensors, ultrasonic
sensors are active which means they emit sound waves to their
surrounding environment. When they receive sound waves re-
ected from moving objects, they can perform detection due to
the change in the wavelength (Guo et al. 2010). Unlike PIR sen-
sors, ultrasonic sensors do not need a direct line of sight since ul-
trasonic waves can travel through the obstacles in a room. ese
sensors are more prone to false positive errors compared to PIR
sensors due to their sensitiveness to movement of inanimate ob-
jects such as blowing curtains (Maniccia and Luan 1994).
Both ultrasonic and PIR sensors can only have a binary output
to indicate the presence of occupant(s). e number of occu-
pants in the space is more oen desired in dierent applications
such as demand-controlled ventilation. An inactive person
would generate CO2 in the rate of 0.3L/min causing the CO2
concentration of a closed space to increase (ASHRAE 2005).
As a result, CO2 sensors’ output have the potential to estimate
the number of occupants in a space by sensing CO2 concentra-
tion (Wang, Burnett, and Chong 1999). e challenge with CO
sensors is that detection has some delay (Arora et al. 2015).
For example Arora et al. (2015) reported 30 min delay in their
experimental setup. e delay in observation might be due to
factors such as airtightness of the room, furniture layout and
distance between the occupants and sensors. Such disruptions
diminish the reliability of CO
sensors for occupancy detection.
Without considering the complex of dierent variables, it will
be misleading to apply models with assumption of perfectly
mixed indoor air. CO2 sensors can also be used to complement
PIR sensors especially when it is hard to provide a direct line
of sight for them (Lam et al. 2009).
Acoustic sensors are passive devices that do not emit energy
but await to detect changes in the energy they receive (Guo
et al. 2010). Unlike ultrasonic sensors that operate with ultra-
sonic waves, acoustic sensors receive energy in form of audible
sounds. ese sensors cannot distinguish between noises gen-
erated by human and other sources therefore have high rate of
false positive errors.
With the recent advancements in image processing techniques,
the video cameras can extract high-resolution occupancy in-
formation including presence, location, number and types of
activities in very high rates of accuracy (Benezeth et al. 2011).
Despite the usefulness of these systems in buiding service man-
Figure 2. Selection of PIR delay period based on the accuracy of
presence detection – adopted from Gunay et al. (2016).
8-227-19 AZIZI ET AL
agement, their application is restricted mainly due to privacy
concerns. e concerns are aggravated when the videos and
information are sent to be processed and stored in a central
server. One solution to resolve the concerns could be to proc-
cess the images locally and send only limited occupancy infor-
mation to the building control system.
Labeodan et al. (2016) experimentally evaluated three types
of chair sensors for occupancy detection. e results showed
that mechanical-switch sensors have better performance than
both strain and vibration sensors. Moreover, chair sensors
outperformed PIR sensors in terms of reliability and accuracy
although their durability and long-term reliability is uncertain
(Labeodan et al. 2016).
Mel et al. (2011) introduced implicit occupancy sensing as us-
ing existing building infrastructure to detect occupancy despite
they are not originally intended for this purpose. e data from
these systems is usually available for building control purposes.
Some examples of such systems include computer network traf-
c, connection of mobile devices to Wi-Fi, security access cards
and usage of elevators. Some other building systems can be used
to measure occupancy but require some modications. Some
examples of such systems are computer keyboard, mouse, web-
cam and microphone while the level of required modication
diers between them. Since such systems are already available
in the buildings, they usually can provide occupancy data with
no cost or little extra cost. e inferences made from each of
these data sources might be unreliable but the aggregation of
data from dierent sources can increase the accuracy and reli-
ability of occupancy detection (B. Dong and Lam 2011; Tiller et
al. 2009). Mel et al. (2011) categorized the implicit occupancy
sensing methods in three dierent tiers:
Tier 1 does not require any modication to existing systems
and data is available to process and use.
Tier 2 requires additional soware to make data accessible.
Tier 3 requires additional soware and hardware produce
and provide data to detect occupancy.
ere are various innovative technologies mentioned in the
literature used for implicit occupancy sensing. RFID (radio
frequency identication) is a technology based on electromag-
netic signal detection that is oen used in security access tags
and cards and is capable to determine the occupants’ location
in a cost eective manner (Zhen et al. 2008). Li and Becer-
ik-Gerber (2011) implies despite the potential of RFID-based
indoor location sensing solutions, widespread implementation
is not possible, as the adaptability to each indoor environment
needs to be further justied. Han et al. (2007) used only humid-
ity sensors to examine occupancy and calibrated the data with
size of windows. e results showed that humidity measure-
ments could potentially disclose occupancy information.
Newsham et al. (2017) examined occupancy in single-occu-
pant oces by using the data that can be easily collected from
occupant’s PC in low-cost such as mouse, keyboard and web-
cam. ese data sources are usually t into tier 2 as their data is
ready to use with small modications. e analysis showed such
data sources could be much more accurate than usual ceiling-
based PIR sensors with over 90% accuracy of occupancy detec-
tion. In this case, high accuracy associate with very low rate of
false negative errors enabled to reduce timeout period. Reduced
timeout period can signicantly increase energy savings in
lighting and HVAC systems. Ghai et al. (2012) used only context
sources that are commonly available in commercial buildings
to infer occupancy. ey used so sensor data (implicit sensor
data) such as Wi-Fi access points, area access badges, calendar
and instant messaging clients. e use of these readily available
data lead to 90% accuracy of presence detection.
Shen, Newsham, and Gunay (2017) count a number of draw-
backs for the conventional single-sensor occupancy detection
e conventional sensors are expensive and require high
investment costs. ey mentioned recent advances in wire-
less systems have decreased the installation cost although
such systems are not as reliable as wired sensors in terms
of data communication and their need to change batteries
is cumbersome.
e conventional sensors oen have low occupant resolu-
tion and do not provide any information on count, identity
and activity of occupants.
e conventional sensors are prone to false detection by a
shadow or ash such as headlight from a passing car.
Building systems that operate by a single sensor can easily
malfunction if the sensor fails indicating the low reliability
of the system.
e outcome from occupancy sensors usually associate with
uncertainty. Instead of using expensive high-end sensors, a
network of dierent sensors can be an answer to many of the
drawbacks assigned to conventional occupancy detection sys-
tems and enable to achieve more reliable and robust determina-
tion of occupancy. Moreover, with propagation of “Internet of
ings” (IoT), the implicit sensors data are more widely avail-
able that more oen have even more uncertainty than conven-
tional sensors. e challenge would be to develop an analysis
method to infer occupancy information from combination of
data from dierent sensors (Dodier et al. 2006).
ere are several methods in the literature for data fusion
and control strategies in real time. Dodier et al. (2006) applied
analysis models based on Bayesian probability theory to de-
termine occupancy from a network of PIR sensors. ey con-
clude this approach oers signicant benet as compared to
single-point sensing. Hailemariam et al. (2011) in their experi-
mental setup in an oce tested a heterogeneous sensor array.
ey used decision trees to classify the sensors and explored the
relationships between them. e results showed improvements
in accuracy when they used data from multiple PIR sensors.
On the contrary, combining dierent types of sensors worsened
the accuracy of detection when they analysed the data with de-
cision trees. Another study in an oce-type environment in
university premises used ambient sensing data such as lighting,
acoustics, motion and CO2 incorporated into an event-based
pattern detection algorithm (Gaussian Mixture Model). e
8-227-19 AZIZI ET AL
results showed the experimented occupancy detection system
could count the number of occupants with 83% accuracy al-
though the maximum number of occupants was 4 and accu-
racy might drop with higher occupant trac. Ghai et al. (2012)
used data from context sources such as area access badges and
Wi-Fi access points to measure occupancy. is study applied
machine-learning techniques including regression and classi-
cation to analyse opportunistic data (implicit sensors data) to
infer occupancy and could achieve 90% accuracy. Ekwevugbe,
Brown, and Fan (2012) used indoor climatic variables, indoor
events and energy data to infer occupancy patterns by using a
novel method from articial intelligence (AI) named Adaptive
Neuro-Fuzzy Inference System. ey conclude more reliabil-
ity is possible with this approach as compared to single-sensor
approach. Markov Chain model which is an approach based
on machine-learning was used in several studies (V L Erick-
son, Carreira-Perpiñán, and Cerpa 2011; B. Dong et al. 2010;
Varick L. Erickson and Cerpa 2010). Dong et al. (2010) tested
three dierent machine-learning approaches in an open-plan
oce building to estimate the number of occupants and could
achieve, in average, 73% accuracy by Hidden Markov models.
Energy eciency potential
Energy saving is found to be the primary reason for applying
occupancy sensing systems in most related literature. e en-
ergy eciency potential of occupancy based energy systems
are also dependent on intensity of occupation in the building.
Table 1. Comparison of research papers investigated energy saving potential of demand-controlled energy systems.
Author(s) Sensing
Climate Type of
V L Erickson,
Perpiñán, and
Cerpa (2011)
of low
80 % Markov chain
42 % _Average of
3 climates
Dong and Lam
Network of
83 % Gaussian
Mixture Model
based Hidden
18.5 % _Humid
Varick L.
Erickson and
Cerpa (2010)
network of
80 % Moving
Markov Chain
20 % _Local
NS* Computer
Newsham et
al. (2017)
Ofce IT
>90 % Machine
16.6–64 % 20–68 % semi-
up ofce
Floyd, Parker,
and Sherwin
PIR sensors NS* _ _ 10–19 % humid
Field study
Agarwal et al.
PIR sensors NS* _10–15 % _Tropical and
San Diego
Goyal, Ingley,
and Barooah
PIR and
ultrasound NS*
_50 % _Humid
Field study
Peng et al.
and indoor
93 % Machine
20.3 % _Tropical
Ofces in
Field study
Dong et al.
PIR sensors 70 % Expectation
nite state
basis functions
20 % _NS* Ofce Field study
* Not specified
8-227-19 AZIZI ET AL
Comparing the irregularly and regularly occupied spaces shows
6–40% dierence in their lighting energy saving potential (Guo
et al. 2010). Climate condition has great inuence on the en-
ergy saving potential associated with applying demand control
HVAC systems (V L Erickson, Carreira-Perpiñán, and Cerpa
2011). Buildings in colder climates are in general more energy
intensive, thus, have more potential to reduce energy use. is
was shown in a study related to university buildings in the
United States based on sensor network occupancy model pre-
dictions. eir investigations in three dierent climates showed
it is possible to achieve, in average, 42% annual energy savings
by using their model for demand controlled HVAC instead of
conventional control (V L Erickson, Carreira-Perpiñán, and
Cerpa 2011). It is worthy of note that baseline control strat-
egy of energy systems has signicant eect on energy saving
potential. Some studies might inate the accuracy and saving
potential by considering overnight periods while vacancy in
commercial buildings is self-evident and attributing energy
saving is irrelevant (Shen, Newsham, and Gunay 2017).
Dong and Lam (2011) deployed a complex environmental
sensor network consist of lighting, acoustics, motion and CO2
sensors in two university buildings. e occupancy output was
fed into a computer model and the comparative simulation
analysis showed 18.5% energy saving compared to conventional
temperature set-point schedule. Another study with similar ap-
proach also found 20% annual energy savings is possible by us-
ing acquired occupancy information in computer simulation of
building (Varick L. Erickson and Cerpa 2010). Newsham et al.
(2017) highlighted the importance of “Internet of ings” and
new emerging data sources that can be used with low cost for
building control and management. A combination of keyboard,
mouse and pixel changes in webcam image showed promising
results for occupancy detection. e enhanced accuracy of the
system compared to conventional PIR sensors could reduce
timeout period from 20 to 5 minutes leading to 25–45% higher
energy saving potential for lighting. Further analysis showed the
possibility to reach up to 64% reduction of HVAC energy use.
Floyd, Parker, and Sherwin (2002) investigated the energy sav-
ings by application of occupancy sensors to lighting control and
found 10–19% energy savings in the commercial buildings. e
summary of abovementioned studies and several other research
projects focused on demand control energy systems and their
entailed energy savings are presented in Table1.
Applications of occupancy sensing
Occupancy detection enables demand-controlled energy sys-
tems that lead to direct energy savings through HVAC and
lighting systems. However, occupancy information can be used
for other applications that may or may not be energy related.
Occupancy information can be used for converting or reusing
spaces by building service designers, for example emergency
evacuation plans (Suter, Petrushevski, and Šipetić 2014). Guo
et al. (2010) mentioned a real- case example of required oc-
cupancy information for reghters to evacuate the elevators
in a building. ey also mentioned the application of occu-
pancy detection in building security and highlighted the use
of network of sensors to enhance the reliability of occupancy
detection to avoid the high cost of false positive errors leading
to false alarm calls to police department.
Occupancy patterns are basic information required for
building design and are important information for building
simulation tools (Crawley et al. 2001). ese simulation tools
are oen used for building energy analysis and design of energy
systems. In order to achieve an optimal design, it is important
to have accurate occupancy pattern for each specic type of
building. Yu et al. (2010) used occupancy information to devel-
op a building energy demand predictive model. Such a model
can lead to accurate prediction of building energy consumption
to improve the energy performance of a building.
Energy use in commercial buildings is strongly related to
their occupancy. Demand response is referred to the eort to
shi the energy peak load and has become more important in
building management due to uctuation of energy prices in dif-
ferent times of a day. Chaney, Hugh Owens, and Peacock (2016)
investigated the inuence of occupancy pattern on applying
demand response and they inferred occupancy information is
important to enable demand response programs. Timm and
Deal (2016) investigated the role of energy information dash-
boards such as occupancy information in changing the energy
behaviours of occupants in a behaviour change campaign. e
occupants behavioural change can lead to signicant energy
savings in commercial buildings without any technical inter-
vention while occupancy information can lead to exploit this
potential (Meier 2006).
With multiple applications of occupancy detection systems,
it will be easier to justify cost of their deployment although dif-
ferent systems must be integrated with each other. Currently in
the existing buildings, even energy systems are not integrated
so that the occupancy data that is used for lighting control can-
not be easily used by HVAC systems without modications.
Implicit data sources are oen described to be easily accessi-
ble and inexpensive to be used to detect occupancy although
there might be hidden costs that are oen not considered by
the researchers. Such data sources are seldom integrated with
the building management systems and required modications
that incur extra expenses. Ghai et al. (2012) mentioned access
control information might be considered sensitive information
and is rarely used for lighting and HVAC control despite being
a promising source of occupancy information. Integration of
dierent data sources with control systems is a major barrier
to use implicit sensing approaches. In future, with increase in
market penetration of wireless sensors, the data would be more
easily accessible through the IoT environment.
Conclusions and areas for further research
Occupancy detection is important approach to tailor the build-
ing services and to improve their eciency. is paper inves-
tigated dierent aspects of occupancy detection in building
management specically with respect to the application in
demand-controlled energy systems. e subjects that are cov-
ered include conventional and emerging occupancy sensors
and methods, implicit occupancy sensing, network of sensors,
multi-sensor data fusion, energy eciency potential of demand-
controlled energy systems and other possible applications for
occupancy sensing systems.
Using a network of dierent sensors is an advantageous ap-
proach to improve the reliability of occupancy detection by us-
ing inexpensive accessible data from implicit sources. ere are
8-227-19 AZIZI ET AL
many dierent multi-sensor data fusion methods to analyse such
data although there is not enough research to compare these
methods for dierent kinds of sensors and data sources. Further
research is required to investigate the optimal use of each fusion
method in respect to dierent combination of data sources and
contextual factors such as probable occupant trac.
e recent advancements in detection sensors to track the
occupants and their activities would open up new applications
and possibilities. Nevertheless, this level of detailed informa-
tion has already caused concerns about privacy issues. Some
examples of the sensors that caused concerns on privacy in-
clude cameras and webcams with image processing features
and security access cards. Future research should consider such
concerns and try to improve the functionality of such technolo-
gies while mitigating the privacy concerns.
e focus on sensing technologies has currently caused a
discrepancy between development of new sensing technologies
and the requirements of applications in practice (Li and Becerik-
Gerber 2011). Some of emerging hot topics such as building-to-
grid integration and using buildings for demand-response may
not require highly detailed and accurate occupancy detection.
It is important that future research involves more deciplines
related to building industry into this area to identify the value
and functionality of sensing technologies by developing their
applications in the built environment.
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... For example, occupancy sensors may be used for space use management by providing information about space use which allows to improve the efficiency of using energy and resources [5]. Occupancy and indoor environmental sensors can be used together to enable solutions such as demandcontrolled energy systems [6], demand response [7], and behavior change campaigns [8]. Multiple applications using similar sensing and data infrastructure increase their chance of being economical and facilitate the widespread adoption of sensors in buildings [6]. ...
... Occupancy and indoor environmental sensors can be used together to enable solutions such as demandcontrolled energy systems [6], demand response [7], and behavior change campaigns [8]. Multiple applications using similar sensing and data infrastructure increase their chance of being economical and facilitate the widespread adoption of sensors in buildings [6]. ...
... The majority of research on applications of PIR sensors take the occupancy detection in offices for granted without investigating the uncertainties in sensor data, while the effectiveness of those solutions is dependent on the reliability of occupancy detection [17]. According to a review study, the energy efficiency potential of demand-controlled energy systems is estimated between 15-50% [6]. However, without reliable occupancy sensing, not only would these savings not be reached, but also the comfort of occupants would be impaired. ...
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The advancements in sensor and communication technologies drive the rapid developments in the applications of occupancy and indoor environmental monitoring in buildings. Currently, the installation standards for sensors are scarce and the recommendations for sensor positionings are very general. However, inadequate sensor positioning might diminish the reliability of sensor data, which could have serious impacts on the intended applications such as the performance of demand-controlled HVAC systems and their energy use. Thus, there is a need to understand how sensor positioning may affect the sensor data, specifically when using multi-sensor devices in which several sensors are being bundled together. This study is based on the data collected from 18 multi-sensor devices installed in three single-occupant offices (six sensors in each office). Each multi-sensor device included sensors to measure passive infrared (PIR) radiation, temperature, CO2, humidity, and illuminance. The results show that the positions of PIR and CO2 sensors significantly affect the reliability of occupancy detection. The typical approach of positioning the sensors on the ceiling, in the middle of offices, may lead to relatively unreliable data. In this case, the PIR sensor in that position has only 60% accuracy of presence detection. Installing the sensors under office desks could increase the accuracy of presence detection to 84%. These two sensor positions are highlighted in sensor fusion analysis as they could reach the highest accuracy compared to other pairs of PIR sensors. Moreover, sensor positioning can affect various indoor environmental parameters, especially temperature and illuminance measurements.
... Overcoming such complexities requires supportive interventions to align stakeholders' motives towards reaching universities' sustainability goals. Such support could be in the form of information and communication technologies (ICT) and the concept of Internet of Things (IoT) that have potential to support decisions in various applications [7]. ...
... Installing demand-control for energy systems was pointed by several interviewees as being an effective measure. Due to the variability of reported energy efficiency potentials [7], there is a need for information tools to provide tailored information on potential energy savings. Using IoT-based feedback tools (for example, space use and/or energy use visualizations) might be an effective strategy to influence decision-makers. ...
... The challenge is to understand how IoT tools can be effectively adapted in organizational and operational processes, as sometimes such knowledge may be missing even after their adoption [2]. Promotion of such tools requires bridging the technical and management aspects on collecting various in-situ data and their visualization [7,10]. ...
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University buildings are relatively energy-intensive. In Sweden, universities usually operate in rented buildings. In this study, interviews were carried out among three categories of stakeholders in a Swedish university to understand their perceptions of energy use and challenges to improve energy efficiency. As per most interviewees, the university’s top management and Akademiska Hus, which owns the buildings, have the main responsibility to reduce the buildings’ energy and carbon footprint. The heads of departments raised the concern on the non-availability of energy data to take actions to reduce energy use. The use of sensors and information technologies to monitor space use, energy use, and indoor environment are attractive to different stakeholders. The implications of the interview results are discussed.
... The prediction of occupancy in a room environment using data from light, temperature, humidity, and CO 2 sensors was tested using several machine learning classification models. It has lately been predicted that the accurate estimation of occupancy detection in the building could save energy from 30% to 42% [34][35][36]. If the occupancy room dataset was applied as an input for HVAC control algorithms, practical measurements indicated energy savings of 37% in [36] and between 29% to 80% in [37,38] shown through an experiment using data from a CO 2 sensor in an office building and additional synthetic data acquired via a simulation method for CO 2 dynamics with randomized occupant behavior. ...
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Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.
The current trend in commercial buildings is moving towards minimizing space utilization, efficient allocation of resources, energy efficiency, better asset management, and increasing productivity by incorporating appropriate technology. The majority of services provided – from ventilation to cleaning and space – are (or should be) a function of occupancy. Yet occupancy is seldom measured in a comprehensive way such that the data can be widely used to improve building operations. There is a wide variety of applications for such data and sensing technologies to collect it, but no existing frameworks for matching the two for optimal life cycle operations. The objective of this paper is to present a framework to select the most appropriate occupancy sensing technologies for a given set of applications. The framework is developed by first identifying the data requirements and characteristics for the selection of applications and defining the characteristics of occupancy sensing technologies, and then analyzing their alignment for optimal occupancy sensing technology selection. This work is built upon a comprehensive review of occupancy sensing technologies and facility management applications. The framework is implemented as a webpage where users can view different occupancy sensing solutions based on the application(s) they select.
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Conference Paper
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Full-text available
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Conference Paper
There are about 7 billion m2 of useful floor space of non-residential (NR) buildings in Europe and 8.1 billion m2 in the USA, consuming 13.6% and 14.9% of the total final energy use, respectively. The NR building sector is more complex and heterogeneous compared to residential buildings and as a result the available information on the NR building stock is very limited in most countries, lacking specific data on building floor areas, construction characteristics, energy use breakdown for different services and end-uses. This gap of knowledge and the limited availability of comprehensive data handicap the efforts to track the energy performance of NR buildings in building stock modeling. The first part of the paper presents an overview of available information for deriving relevant benchmarks in publicly accessible databases in Europe (e.g. Eurostat, EU building stock observatory, Odysee-Mure) and others available from the USA (e.g. CBECS, ASHRAE). The final energy use intensity of NR buildings averages 268.3 kWh/m2 in Europe (with large deviations among the different countries) and 252.0 kWh/m2 in the United States. The second part summarizes the calculated energy use intensities for NR buildings in Greece, using data from energy performance certificates. The available data is clustered and analyzed for thirty building uses, different construction periods and climate zones. The average primary (final) energy use intensity is 539.5 (250.3) kWh/m2 while the emissions reach 170.0 kgCO2/m2. Access the PAPER at Access the PRESENTATION at The Conference Proceedings are available at
Occupancy in buildings is one of the key factors influencing air-conditioning energy use. Occupant presence and absence are stochastic. However, static operation schedules are widely used by facility departments for air-conditioning systems in commercial buildings. As a result, such systems cannot adapt to actual energy demand for offices that are not fully occupied during their operating time. This study analyzes a seven-month period of occupancy data based on motion signals collected from six offices with ten occupants in a commercial building, covering both private and multi-person offices. Based on an occupancy analysis, a learning-based demand-driven control strategy is proposed for sensible cooling. It predicts occupants' next presence and the presence duration of the remainder of a day by learning their behavior in the past and current days, and then the predicted occupancy information is employed indirectly to infer setback temperature setpoints according to rules we specified in this study. The strategy is applied for the controls of a cooling system using passive chilled beams for sensible cooling of office spaces. Over the period of two months both a baseline control and the proposed demand-driven control were operated on forty-two weekdays of real-world occupancy. Using the demand-driven control, an energy saving of 20.3% was achieved as compared to the benchmark. We found that energy savings potential in an individual office was inversely correlated to its occupancy rate.
A primary strategy for the energy-efficient operation of commercial office buildings is to deliver building services, including lighting, heating, ventilating, and air conditioning (HVAC), only when and where they are needed, in the amount that they are needed. Since such building services are usually delivered to provide occupants with satisfactory indoor conditions, it is important to accurately determine the occupancy of building spaces in real time as an input to optimal control. This paper first discusses the concepts of building occupancy resolution and accuracy and briefly reviews conventional (explicit) occupancy detection approaches. The focus of this paper is to review and classify emerging, potentially low-cost approaches to leveraging existing data streams that may be related to occupancy, usually referred to as implicit/ambient/soft sensing approaches. Based on a review and a comparison of related projects/systems (in terms of occupancy sensing type, resolution, accuracy, ground truth data collection method, demonstration scale, data fusion and control strategies) the paper presents the state-of-the-art of leveraging existing occupancy-related data for optimal control of commercial office buildings. It also briefly discusses technology trends, challenges, and future research directions.
We explored methods of detecting occupancy in single-person offices using data already collected by the occupant’s PC, or data from relatively cheap sensors added to the PC. We collected data at 15-second intervals for up to 31 days in each of 28 offices. A combination of low/no cost sensors (webcam-based motion detection, and keyboard and mouse activity) was much more accurate at detecting occupancy than a commercial ceiling-based passive infrared (PIR) sensor, and provided overall daytime accuracy >90%, with very low false negative rates. This enhanced detection performance would enable a reduction in the timeout periods for building service curtailment on space vacancy. For example, lighting switch-off timeout could be reduced from the current energy code standard of 20 minutes to less than 5 minutes, increasing energy savings potential by 25-45%. We then deployed this system in a proof-of-concept demonstration, using it to control lighting, heating, ventilation, and air conditioning (HVAC), and plug loads in a mock-up office environment. Tests were run over nine occupied days (six in cooling season, three in heating season). The system delivered energy savings of 15-68%, with no reported false negative errors.
Technical Report
The content of the National Energy Code for Buildings 2011 (NECB) was approved by the Canadian Commission on Building and Fire Codes (CCBFC) at its April 2011 meeting. During the development of the material, some jurisdictions commented that they had specific policy directives regarding energy efficiency in buildings that they needed to address. It was agreed that a document would be developed to provide guidance on how the NECB 2011 can be modified to address those directives. As a result, this document was developed by a Joint Task Group of the CCBFC and the Provincial/Territorial PolicyAdvisory Committee on Codes (PTPACC). The document is targeted to the Provinces and Territories, however it can also be used as a reference document by designers and owners in order to see the effects of varying certain components. It must be remembered that no section of the document can stand alone and therefore the document must be used in its entirety.
Building energy use research has largely been focused on technological performance despite growing evidence that human behavior has an equally significant role (Sovacool, 2014) [34]. This paper aims to address this by examining how the role of real-time information affects building occupant attitudes and behaviors toward energy use. Four buildings located on four different community college campuses in Illinois were outfitted with a centrally located graphic display of the building’s real-time energy use (an energy dashboard) and implemented a 6-week energy behavior change campaign. Intervention efficacy was tested with an online survey that was distributed to each campus population before and after the intervention. Pre-post analysis, comparison between exposed and unexposed populations, and cross-campus comparisons were then conducted. Our findings show that although the interventions resulted in significant energy savings (7–10% in electricity and 50% decrease in natural gas), differences in student and faculty/staff energy attitudes or behaviors proved insignificant. Post-intervention longitudinal interviews with building facility managers, however, showed that energy dashboards improved their ability to detect system faults that led to their implementation of energy-saving building adjustments. While energy dashboards can be effective at improving facility management approaches, they are less useful for measurably affecting occupant attitudes and behaviors.
The use of accurate and fine-grained occupancy information in building operation can, in addition to providing visualization of space use, provide worthwhile energy savings when the operation of lighting, heating, ventilation and air-conditioning systems are tailored to actual building occupancy information. Although there are ample off-the-shelf heterogeneous occupancy sensors available for use in practice, the information provided is often coarse-grained and inaccurate. As a result, multiple sensors, which cost more to install and maintain are often used in building operation for occupancy driven control of lighting, heating, ventilation and air-conditioning systems (L-HVAC). This article presents results from the experimental evaluation of chair sensors using sensing techniques based on strain, vibration and a mechanical-switch for occupancy detection in an office space. In addition, results from the application of one of the chair sensors in an open-plan office space as a heterogeneous occupancy detection system for occupancy-driven control of the lighting system in the space is as well provided.