ICT Technologies, Techniques and Applications to Improve Energy
Efficiency in Smart Buildings
C
´
esar Benavente-Peces
1 a
and Nisrine Ibadah
2 b
1
ETS de Ingenier
´
ıa y Sistemas de Telecomunicaci
´
on, Universidad Polit
´
ecnica de Madrid,
Calle de Nikola Tesla sn, 28031 Madrid, Spain
2
LRIT Laboratory, Associated Unit to CNRST (URAC 29), IT Rabat Center, Faculty of Sciences,
Mohammed V University in Rabat, Morocco
Keywords:
Smart Building, Energy Supply, Energy Distribution, Energy Management, Energy Efficiency, Energy
Savings, Sustainability, Green Energy, Energy Storage.
Abstract:
Currently, most of the human activities impact the environment. Worldwide sustainable development is re-
quired to preserve a good quality of life. Energy efficiency is one of the most relevant issues that the scientific
community and society must face along the next decades. This paper focuses on reviewing and noting the
main factors which impact the optimization of electrical energy efficiency in Smart Buildings, including distri-
bution, consumption analysis, strategies and management. Smart grids and smart buildings are playing a key
role in the definition of the following generations of cities where the impact of energy consumption on the en-
vironment must be reduced as much as possible. Notwithstanding, all the factors impacting the production and
distribution must be also taken into consideration by energy production companies and distribution companies
as well. Green energies are being introduced in smart cities and buildings, only slower than required, and in
general, focusing on the consumption side asking for higher performance monitoring and control techniques,
and encouraging to incorporate energy harvesting initiatives to improve the overall efficiency. In this paper,
the major target is pointing out all the relevant factors influencing smart building energy efficiency, up to the
consumer side and, at the same time, paying attention on distribution and generation issues and, specifically,
available communication standards, technologies, techniques, algorithms, which enable high performance sys-
tems to optimize energy consumption and occupant comfort.
1 INTRODUCTION
Environmental sustainability requires minimizing the
impact of human activities on the region where they
take place. Energy efficiencyy is one of the factors
which most impacts on the energy pollution reduc-
tion goal. In this paper the main focus is the elec-
trical energy efficiency, especially how ICT (Infor-
mation and Communication Technologies) can offer
a proper support to energy distribution and consump-
tion. When intelligence is introduced in the distribu-
tion part, the term Smart Grids is used. Its improve-
ment requires the interaction with the occupants and
the different elements, devices, energy sources, me-
tering, etc., to collect relevant information of the en-
vironment including external data sources as weather
sensing systems and energy supplier performance.
a
https://orcid.org/0000-0002-2734-890X
b
https://orcid.org/0000-0002-3079-3115
Furthermore, the addition of intelligent features to
the smart building, by using artificial intelligence and
machine learning techniques and engineering skills,
grants the smart building the capability to learn from
the performance history (which could be featured by
new analytic techniques) while making decisions in
real-time to achieve the highest energy use efficiency
(Karkare et al., 2014).
The use of new technologies as smart energy me-
tering (electricity and gas), smart lighting as part
of the smart grid, renewable energies, low power
consumption equipment (printers, HVAC, appliances,
etc.) combined with green energies will contribute to
energies efficiency (Bhutta, 2017).
Figure 1 depicts the block diagram of the sys-
tem architecture used to optimize energy consumption
by using data analytics and high-performance algo-
rithms. Heterogeneous raw data are collected at Data
collection system which stores the data onto the Data-
Benavente-Peces, C. and Ibadah, N.
ICT Technologies, Techniques and Applications to Improve Energy Efficiency in Smart Buildings.
DOI: 10.5220/0009000601210128
In Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), pages 121-128
ISBN: 978-989-758-403-9; ISSN: 2184-4380
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
121
Figure 1: Example of the system architecture.
Base. The processing unit Data processing consists
of a high-performance algorithm which processes the
data and combines with the Data Analytics to make
decisions, based on artificial intelligence and machine
learning. Data analytics, management and production
tool are used to identify trends and predict tendencies.
2 ENERGY SAVING ESTIMATION
Table 1 shows different technologies which are used
in smart buildings to achieve higher energy efficien-
cies. The combination of ICT and intelligent ma-
terials increase the savings in energy, and it’s the
best strategy to improve the overall building effi-
ciency (King and Perry, 2017).
In concrete, data analytics can process the data
coming from several sources/sensors, event from sur-
rounding smart buildings, in order to optimize the
parameters regulating the different energy generat-
ing/consuming items in order to seek for the highest
performance and energy savings. The larger the avail-
able data to be processed, the higher the accuracy of
the action to take.
3 TECHNOLOGIES AND
TECHNIQUES FOR
IMPROVING ENERGY
EFFICIENCY
The deployment of smart and energy-efficient build-
ings is sustained by the development of the appropri-
ate technologies, techniques and devices with suitable
features.
3.1 Green Energies
Green Energies energize buildings independently of
external power sources. Currently, the most common
situation is a hybrid configuration (Billanes et al.,
2018). The term Distributed generation concerns the
use of several energy harvesting on-site sources at
each smart buildings to produce its energy (electric-
ity). Figure 2 depicts the yearly electricity consump-
tion in commercial buildings and the expected saving
as well
1
. The largest savings are achieved in light-
ing (Bonneau et al., 2017).
In (Matiko et al., 2013) the authors introduce a
study focussing on energy harvesting current state-
of-the-art and application, so far. Additionally to the
traditional energy harvesting technologies and tech-
niques, as using solar panels, thermal exchanger, etc.,
highlighting those new technologies showing a rele-
vant potential to be used as energy sources like: elec-
tromagnetic waves, kinetic, thermal, aire flow. The
results for most common harvesting techniques:
indoor solar cell (active area of 9 cm
2
, volume of
2.88 cm
3
): approx. 300 µW from a light intensity
of 1000 lx;
thermoelectric harvester (volume of 1.4 cm
3
): 6
µW from a thermal gradient of 25
o
C;
periodic kinetic energy harvester (volume of
0.15 cm
3
): 2 µW from a vibration acceleration of
0.25 ms
2
at 45 Hz;
electromagnetic waves harvester (13 cm antenna
length and energy conversion efficiency of 0.7): 1
µW with an RF source power of -25 dBm;
airflow harvester (wind turbine blade of 6 cm di-
ameter and generator efficiency of 0.41): 140 mW
from an airflow of 8 m 1.
1
Source: Lutron.
SENSORNETS 2020 - 9th International Conference on Sensor Networks
122
Table 1: Smart technologies and materials energy savings in buildings.
System Technology Energy savings
HVAC Variable speed control 15-50 % of pump or motor energy
HVAC Smart ambient sensing 5-10 %
Plug load Smart plug 50-60 %
Plug load Advanced power strip 25-50 %
Lighting Sensors, actuators smart control 45%
Lighting Web-based management 20-30% above controls savings
Window shading Automated shade system 21-38%
Windows hading Switchable film 32-43%
Windows hading Smart glass 20-30%
Building automation Building automation system 10-25% whole building
Analytics Cloud information-based 5-10% whole building
Figure 2: Commercial buildings energy use breakdown and predicted savings.
3.2 Communications in Smart Buildings
The advances in ICT technologies and techniques
open high expectations about energy efficiency goals
(Telekom, 2015). Wired technologies have no flex-
ibility: Any change in the network distribution in-
volves new wiring, causing a high maintenance cost.
However, they can be very robust in the case of re-
ceiving interferences of other systems. Despite well-
known most used wireless standards, e.g., Wi-Fi,
Bluetooth, Zigbee, EnOcean, Thread or KNX
2
, some
protocols are aimed at dedicated tasks in smart build-
ings, e.g., DALI and Mobus protocols are protocols
aimed at setting on/off lighting system; Opentherm
was specifically developed for heating/cooling sys-
tems control. And MBus is used in smart meter-
ing devices (Saritha and Sarasvathi, 2017), (Sethi and
Sarangi, 2017).
Table 2 shows some features of the most com-
monly used IoT technology standards (Dr.R and
Velumani, 2014). Some standards, as NFC (Near
2
KNX is an open standard (see EN 50090, ISO/IEC
14543) for commercial and domestic building automation.
Field Communications) are appropriate for indoor fa-
cilities data exchange, given the range is up to a few
metres. Others, operate up to hundreds of metres
(WiFi, Bluetooth, Zigbee, etc.). These standards can
be used for indoor/outdoor communication at a build-
ing level, i.e., without connecting-to external net-
works or resources. Long-range standards are appro-
priate to connect to other buildings.
3.3 Artificial Intelligence and Big Data
Artificial intelligence and machine learning tech-
niques allow Smart Buildings adapting to occupants
needs. Using high-performance algorithms for intel-
ligent energy management buildings will increase the
comfort, efficiency, resilience and safety (Siemens,
2018). To optimize energy consumption and effi-
ciency, Smart Buildings must manage: Energy (smart
metering, demand responsive systems); Lighting and
elevators (daylight meters, presence sensors, lift de-
mand); Issues detectors (sensors to detect fire, smoke,
watering); Smart metering (electricity water, gas);
Monitoring (parking lot occupancy, security); Am-
ICT Technologies, Techniques and Applications to Improve Energy Efficiency in Smart Buildings
123
Table 2: IoT Technology Standards.
RFID NFC WiFi ZigBee Blue-tooth WSN
Network PAN PAN LAN LAN PAN LAN
Topology P2P P2P star Mesh,star,tree star Mesh, star
Power Very low Very low Low-high Very low low Very low
speed 400 kbs 400 kbs 11-10 Mbs 250 kbs 700 kbs 250 kbs
Range (meters) < 3 < 0.1 4-20 m 10-300 m < 30 m 200 m
bient comfort (lighting, HVAC). Continuous energy
consumption metering devices can improve energy ef-
ficiency based on: improved real-time energy con-
sume visualization, instantaneous energy manage-
ment monitoring and control.
Advanced analytics tools can help to discover hid-
den insights from your raw data coming from differ-
ent sources in the smart building, e.g., sensors, me-
tering devices. Given a database where raw data is
stored, the Analytics tools process and transform huge
amounts of raw data into remarking reports and dash-
boards. These tools allow tracking the key defined or
identified energy efficiency metrics, providing feed-
back regarding long-term trends, highlight outliers,
and uncover hidden insights. Based on this set of in-
formation, the algorithms based on artificial intelli-
gence and machine learning will interact with the dif-
ferent components of the smart building to increase
energy efficiency and improve comfort.
Modelling energy building profile enables devel-
oping ad-hoc strategies. Key parameters are iden-
tified, and algorithms are developed to process the
collected data and make decisions based of the his-
tory and the result of predictive models (Ahrens et al.,
2016), which are useful to design the proper strategies
for higher energy efficiency..
4 FUTURE SMART BUILDING
CHALLENGES
Connectivity contrbutes to achieve higher energy ef-
ficiencies. A cloud of smart buildings (CoSB) allows
sharing information among the connected facilities.
The main benefits are:
Data aggregation from different sources pro-
vide additional information to the analytics tool
achieving higher performance.
The CoSB concept will help to define a scalable
architecture easier to manage.
Smart building sensing capabilities, and sharing
the collected data could be especially suitable for
handicapped citizens.
Provided smart buildings are equipped with a vari-
ety of sensors, electronics, actuators, etc., an im-
mense volume of data (big data) is continuously
generated.
Smart building solution includes automation and
real-time analytics which provides reach informa-
tion, higher accuracy and energy saving, and effi-
ciency optimization.
Easy integration with, e.g., other buildings to cre-
ate the CoSB, the smart grid, smart city, etc.
Due to connectivity needs, the following are very ac-
tive fields: Interoperability, cybersecurity and data
privacy.
In (Pilgrim, 2019), the author identifies some key
challenges (among others) which must be faced to
achieve the expected performance:
Better and more accurate weather prediction
methods and techniques at local level.
Development of algorithms capable to manage
data more efficiently based on AI and ML.
Improvement of thermal modelling techniques,
more accurate and at a lower cost.
Regarding the security of smart buildings, there are
key issues where more efforts are required to guaran-
tee the appropriate level of security. Among them, it
is worthy to remark:
gateways which interconnect buildings to the grid,
cyber-risk of connected devices,
detecting and preventing particular attacks,
ensure secure interoperability between protocols,
use of AI and ML to improve security,
privacy, confidentiality, availability, non-
repudiation.
data integrity, cryptography.
5 DISCUSSION
Smart buildings highest accuracy is achieved when
the appropriate resources are used to achieve the high-
est energy efficiency. The main features which char-
acterize the current state-of-the-art of smart buildings
rely on the following key elements:
SENSORNETS 2020 - 9th International Conference on Sensor Networks
124
High-performance Computer. It is the hardware
which hosts the required algorithms and processes
the collected data to make decisions.
Data Analysis and Decision Making Software
Tools. It is the main core of the intelligence sys-
tem which can receive the data collected by dif-
ferent sensors and measuring elements, and other
relevant data from other sources of information
such as networks from information nearby build-
ings sensors.
Among other functions, this intelligent system
must be able to analyse data from different
sources, whether internal or external to the build-
ing, to make a more precise decision.
Advanced Analytics Tools. These techniques
are currently used to obtain multiple information
from among all the data collected, they can even
determine trends to be able to anticipate certain
events, for example, a sudden change in temper-
ature outside the smart building. This task will
be carried out with greater success the more data
from the environment are obtained, for example,
the data collected by nearby buildings.
Sensors Network. that allows us to obtain the
maximum possible information from the environ-
ment. For this, different types of environmental
sensors should be available, focusing on the en-
ergy management of ventilation, cooling and heat-
ing systems. In other cases, it will be important to
have the possibility of measuring lighting levels,
i.e., light intensity.
Measuring Devices. It is important to know the
instantaneous energy consumption. However, to
have two systems that are more precise and, above
all, capable of managing the available energy re-
sources in the most efficient way possible, it is
necessary to have a consumption history to allow
data analysis tools to make decisions in historical
consumption function, current consumption and
consumption forecast. Moreover, similar actions
could be performed for every single device, being
able to reach the maximum possible granularity
in the energy management and comfort provided
by occupants of the building, at the cost of an in-
crease in the deployment price.
Communication Infrastructure. This is the back-
bone of the system. This system has a capital role
in the smart building. It is responsible to provide
the appropriate infrastructure to warranty the flow
of data among the different elements which com-
pose smart buildings.
Figure 3 shows a possible block diagram where are
all the elements in the smart building are depicted. A
database could be available to keep the history of the
collected data which could be used for different pur-
poses: failure prediction, identification of facilities
consuming more power, energy demand trends, etc.
Sensing, metering and actuator devices are connected
to a gateway given various communication standards
are being used. The firewall must provide the appro-
priate security.
The terms reliability and resilience are related and
when describing the definition of one of them the
other arises (Albasrawi et al., 2014), (Haught and
Paladino, 2012). An option to improve the reliabil-
ity of the smart grid is deploying redundancy by man-
aging different sources. Furthermore, this approach
also has an additional advantage, it increases system
resilience. On the other hand, redundancy also in-
creases the deployment cost.
Data collection, processing and sharing are re-
quired to enhance the overall building facilities and
resources more accurately and reliably. Connectiv-
ity among the different sensors, actuators, metering
devices, processing and storing units, intelligent data
processing systems must be interconnected. For such
purpose there are several possibilities, as shown in ta-
ble 3 where the advantages and disadvantages of com-
monly used technologies are remarked. For each con-
crete scenario, a concrete set of technologies will be
the most appropriate and the overall solution would
be a heterogeneous communications system.
Energy efficiency can be Implement by very sim-
ple electronics. For example, the data regarding peo-
ple motion and occupancy or facilities obtained by ap-
propriate sensor devices can be easily used to regulate
the air conditioning system, ventilation and lighting
levels in real-time. as consequence, The ventilation
flow, air conditioner temperature, and light intensity
can be regulated with the basic intelligence improving
the environment for comfort and productivity of the
individuals who are occupying the facilities and, at
the same time the energy consumption level reduces
the cost of the energisation.
To achieve further savings it is required including
additional intelligence in the system. For example,
the system can continuously measure the temperature
of a facility and based on the information provided
by the sensors it can take a more accurate action con-
sidering hysteresis cycles and the information corre-
sponding to the history on their records taking along
a given period of time, stored by the system to be
processed by the processing unit to carry out a data
analytics to make the appropriate decisions based on
artificial intelligence and machine learning high per-
formance algorithms.
ICT Technologies, Techniques and Applications to Improve Energy Efficiency in Smart Buildings
125
Figure 3: Overall view of the smart building connectivity both at building and cloud level.
Table 3: Advantages and drawbacks of widely used and well established technologies.
Bluetooth WiFi ZigBee THREAD
Pros
Low energy Well established
standards
Low energy Low energy
Available on mobile
devices
Available on mobile
devices
Well established
standards
Low energy
IPv6 based Good range Mesh network Good range
IPv6 based Good range IPv6 based
Cons
Star network Star network Not IP based Not well established
compared to ZigBee
Short range Not available on mo-
bile phones
Not available on mo-
bile phones
Not mature
6 CONCLUSIONS
Information and communications technologies are
playing a relevant role in energy efficiency. So far,
smart devices such as thermostats, temperature sen-
sors, light intensity meters, presence detectors, etc.,
have been employed to provide energy savings by
switching on/off lights or managing HVAC in simple
ways.
A large amount of information about buildings
state allows making more accurate decisions to more
efficiently manage the energy and building resources
(including energy harvesting).
Connectivity is a critical feature in smart build-
ings, where sensors, actuators, electromechanical el-
ements, databases, information processing system,
etc., must be interconnected. For this purpose, wire-
less technologies are the most suitable as they provide
greater flexibility and a lower deployment cost. IoT
are identified as the most suitable due to their exten-
sive use in many common systems and devices, such
as smartphones, allowing easy integration of devices
and systems.
Interoperation between wireless standards is often
required and, therefore, a gateway that allows interop-
erability. Moreover, since smart buildings can receive
information from the environment and other nearby
smart buildings, Cloud of Buildings, it may even be
necessary to use cellular technologies. The security
of smart buildings against cyberattacks is one of the
aspects that is being faced in recent years to provide
the appropriate means to avoid cyber-risks and do not
compromise either the safety of people, or the instal-
lations, or of the personal data.
The overall goal of this paper is showing the ca-
pabilities of ICT supporting technologies to optimize
the energy consumption in buildings. Currently, these
possibilities are not being systematically exploited al-
though the appropriate tools are available, as claimed
in this work.
To conclude, it is worthy to note the following re-
marks:
SENSORNETS 2020 - 9th International Conference on Sensor Networks
126
Intelligent applications in the building sector, and
supported by ICT, resulting in an intelligent build-
ing which can save energy by increasing their ef-
ficiency, and, additionally, offering handicapped
people additional support.
Identify smart technologies and applications
which most optimize energy efficiency (highest
energy savings) and are most cost-effective, and
provide higher comfortability.
Smart buildings are ready to interconnect and in-
tegrate into Smart Grids and Smart Cities.
They contribute to sustainability.
Improves people comfort and well.being.
On the other hand, the main smart buildings benefits
regarding energy efficiency are:
Energy management by using novel ICT tech-
nologies allows optimizing energy consumption
and billing.
Energy consumption control by setting on/off ap-
pliances: lighting, HVAC, etc.
Data collection for processing and make decisions
to control smart windows, occupancy detectors,
temperature, etc.
Efficient use of facilities to optimize energy con-
sumption.
Safety and security efficiency are a key motivation
in deploying smart buildings.
Individuals access control is needed and can be
improved by using intelligent systems by moni-
toring and track people and things
3
.
Finally, it is worthy to make some remarks about IoT
in smart buildings applications:
IoT is not a concrete technology or device aimed
at a specific application, but a set of tools with
different capabilities (standards) which include
connectivity capabilities according to the stan-
dard each one meet, capable to collect, trans-
mit/receive, sometimes process, and share data
(using sensors and actuators).
Relevant features of IoT devices are energy effi-
ciency and wireless connectivity (sometimes en-
ergy harvesting).
3
INVISUM (INtelligent VIdeoSUveillance SysteM): It
was a project aimed at monitoring facilities for security pur-
poses funded by the Spanish government. Partners: Moviq-
uity, NVISION, Universidad Polit
´
ecnica de a Madrid, Uni-
versidad Rey Juan Carlos. UPM’s research group involved
in this project was directed by C
´
esar Benavente-Peces.
For each specific application/environment devel-
opers must point out the smart technologies which
most optimize energy efficiency, providing the
most reliable link and cost-effective.
In many applications, e.g. smart buildings, using
heterogeneous wireless technologies are needed
to meet the various requirements for specific tasks
(sensing, tracking, etc.) in distinct environments.
Standards aimed at low power consumption, low
data-rate and short-range (personal area network)
technologies are specifically useful in sensors de-
ployment.
Long-range standards as cellular
(2G/3G/4G/5G/LTE), WiFi, LoRa and low-
power, long-range wide-area communication
technologies could play a main role in smart
buildings connectivity by collecting and deliver-
ing the data to the cloud.
By using novel approaches based on artificial in-
telligence and machine learning, and analytics
techniques, it is feasible achieving the higher ac-
curate analysis of the building and providing more
precise response, resulting in the best comfort to
all the occupants and higher energy efficiency.
The next step will be the extension of the CoSB con-
cept as a reality to achieve the efficient energy con-
sumption goal.
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