ICT Technologies, Techniques and Applications to Improve Energy

Efﬁciency in Smart Buildings

esar Benavente-Peces

1 a

and Nisrine Ibadah

2 b

ETS de Ingenier

ıa y Sistemas de Telecomunicaci

on, Universidad Polit

ecnica de Madrid,

Calle de Nikola Tesla sn, 28031 Madrid, Spain

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.

https://orcid.org/0000-0002-2734-890X

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

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

. 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

, volume of

2.88 cm

): approx. 300 µW from a light intensity

of 1000 lx;

• thermoelectric harvester (volume of 1.4 cm

): 6

µW from a thermal gradient of 25

• periodic kinetic energy harvester (volume of

0.15 cm

): 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.

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

, 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

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

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• 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

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).

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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