Reducing High Energy Demand Associated with Air-Conditioning Needs in Saudi Arabia

Abstract

Electricity consumption in the Kingdom of Saudi Arabia (KSA) has grown at an annual rate of about 7% as a result of population and economic growth. The consumption of the residential sector accounts for over 50% of the total energy generation. Moreover, the energy consumption of air-conditioning (AC) systems has become 70% of residential buildings’ total electricity consumption in the summer months, leading to a high peak electricity demand. This study investigates solutions that will tackle the problem of high energy demand associated with KSA’s air-conditioning needs in residential buildings. To reduce the AC energy consumption in the residential sector, we propose the use of smart control in the thermostat settings. Smart control can be utilized by (i) scheduling and advance control of the operation of AC systems and (ii) remotely setting the thermostats appropriately by the utilities. In this study, we model typical residential buildings and, crucially, occupancy behavior based on behavioral data obtained through a survey. The potential impacts in terms of achievable electricity savings of different AC operation modes for residential houses of Riyadh city are presented. The results from our computer simulations show that the solutions intended to reduce energy consumption effectively, particularly in the advance mode of operation, resulted in a 30% to 40% increase in total annual energy savings.

Full text

energies
Article
Reducing High Energy Demand Associated with
Air-Conditioning Needs in Saudi Arabia
Jubran Alshahrani * and Peter Boait
School of Engineering and Sustainable Development, De Montfort University, Leicester LE1 9BH, UK;
p.boait@dmu.ac.uk
*Correspondence: jubran.alshahrani@my365.dmu.ac.uk; Tel.: +44-7472424404
Received: 30 October 2018; Accepted: 13 December 2018; Published: 28 December 2018


Abstract:
Electricity consumption in the Kingdom of Saudi Arabia (KSA) has grown at an annual
rate of about 7% as a result of population and economic growth. The consumption of the residential
sector accounts for over 50% of the total energy generation. Moreover, the energy consumption of
air-conditioning (AC) systems has become 70% of residential buildings’ total electricity consumption
in the summer months, leading to a high peak electricity demand. This study investigates solutions
that will tackle the problem of high energy demand associated with KSA’s air-conditioning needs in
residential buildings. To reduce the AC energy consumption in the residential sector, we propose the
use of smart control in the thermostat settings. Smart control can be utilized by (i) scheduling and
advance control of the operation of AC systems and (ii) remotely setting the thermostats appropriately
by the utilities. In this study, we model typical residential buildings and, crucially, occupancy behavior
based on behavioral data obtained through a survey. The potential impacts in terms of achievable
electricity savings of different AC operation modes for residential houses of Riyadh city are presented.
The results from our computer simulations show that the solutions intended to reduce energy
consumption effectively, particularly in the advance mode of operation, resulted in a 30% to 40%
increase in total annual energy savings.
Keywords: air-conditioning; peak demand; renewable energy; Saudi Arabia; occupancy behavior
1. Introduction
The Kingdom of Saudi Arabia (KSA) has a desert climate that is characterized by high heat
during the day and a temperature drop at night; the heat rapidly increases after sunrise and stays so
until sunset. In a year, there are mainly two seasons: winter and summer. In summer, the average
temperature is about 45 ◦C, but the ambient air temperatures could reach up to 50 ◦C [1].
The electricity sector in KSA is faced with the great challenge of meeting the increasing electricity
demand. Over 50% of the Kingdom’s total electricity production is consumed by the residential sector.
Moreover, over the last few decades, the growth in energy consumption is approximately 7% annually,
and 60% to 70% of the energy consumed by residential buildings is due to the air-conditioning systems
during the summer months [
2
,
3
]. Figures 1and 2show KSA’s typical daily electricity load curve
during the summer and winter, respectively [
4
]. As can be observed in Figure 1, the peak demand for
electric power in KSA occurs midday, between 11:00 and 17:00 (from May to September). The surges
during that period of the day in summer is non-existent in the winter (e.g., the peak time in winter is
14:00–15:00).
Energies 2019,12, 87; doi:10.3390/en12010087 www.mdpi.com/journal/energies

Energies 2019,12, 87 2 of 29
Energies 2018, 11, x FOR PEER REVIEW 2 of 30
respectively. It can also be noticed that ACs are used day and night during the summer (at 19:00 in
the night on the weekday, the demands are 51 GW and 30 GW in summer and winter, respectively).
Thus, to meet the peak demand, an additional generation capacity of 2 to 5 GW is added each year to
meet the country’s growing electricity demands (ECRA, 2015), as the current committed capacity will
not be sufficient to meet the projected demand, according to King Abdullah City for Atomic and
Renewable Energy (KACARE).
Figure 1. Kingdom of Saudi Arabia’s (KSA) typical daily electricity load curve during summer [4]
.
Figure 2. KSA’s typical daily electricity load curve during winter [4].
Around 70% of KSA’s buildings are not thermally insulated, according to Saudi Energy
Efficiency Centre (SEEC) [5], which is a major cause of the high electricity consumption.
Consequently, a huge part of air-conditioning loads is the heat transmission through the walls of
buildings and roofs [6]. Another huge contributor to the high electricity consumption is occupant
behavior. For instance, many of the customers in KSA leave their air-conditioning units to run non-
stop throughout the summer months [7,8]. Over 73% of households in KSA turn on their air-
conditioning systems from between 10 and 24h on a typical day during the summer months [9]. A
significant amount of energy saving can be achieved even by a small increase of AC thermostat
settings, e.g., 1 °C or 2 °C [10,11]. These savings are particularly important and desirable during the
peak duration of electricity demands. The setting of the thermostats to one value throughout the
summer and to another value throughout the winter (with the two values determined by averaging
the daily optimal values) can significantly reduce air-conditioning loads and increase energy savings
Figure 1. Kingdom of Saudi Arabia’s (KSA) typical daily electricity load curve during summer [4].
Energies 2018, 11, x FOR PEER REVIEW 2 of 30
respectively. It can also be noticed that ACs are used day and night during the summer (at 19:00 in
the night on the weekday, the demands are 51 GW and 30 GW in summer and winter, respectively).
Thus, to meet the peak demand, an additional generation capacity of 2 to 5 GW is added each year to
meet the country’s growing electricity demands (ECRA, 2015), as the current committed capacity will
not be sufficient to meet the projected demand, according to King Abdullah City for Atomic and
Renewable Energy (KACARE).
Figure 1. Kingdom of Saudi Arabia’s (KSA) typical daily electricity load curve during summer [4]
.
Figure 2. KSA’s typical daily electricity load curve during winter [4].
Around 70% of KSA’s buildings are not thermally insulated, according to Saudi Energy
Efficiency Centre (SEEC) [5], which is a major cause of the high electricity consumption.
Consequently, a huge part of air-conditioning loads is the heat transmission through the walls of
buildings and roofs [6]. Another huge contributor to the high electricity consumption is occupant
behavior. For instance, many of the customers in KSA leave their air-conditioning units to run non-
stop throughout the summer months [7,8]. Over 73% of households in KSA turn on their air-
conditioning systems from between 10 and 24h on a typical day during the summer months [9]. A
significant amount of energy saving can be achieved even by a small increase of AC thermostat
settings, e.g., 1 °C or 2 °C [10,11]. These savings are particularly important and desirable during the
peak duration of electricity demands. The setting of the thermostats to one value throughout the
summer and to another value throughout the winter (with the two values determined by averaging
the daily optimal values) can significantly reduce air-conditioning loads and increase energy savings
Figure 2. KSA’s typical daily electricity load curve during winter [4].
Comparing the two figures further, the demands at 13:00 in the summer and winter during the
weekdays are 52 GW and 26.5 GW, respectively; during the weekend, 56 GW and 28 GW, respectively.
It can also be noticed that ACs are used day and night during the summer (at 19:00 in the night on the
weekday, the demands are 51 GW and 30 GW in summer and winter, respectively). Thus, to meet the
peak demand, an additional generation capacity of 2 to 5 GW is added each year to meet the country’s
growing electricity demands (ECRA, 2015), as the current committed capacity will not be sufficient
to meet the projected demand, according to King Abdullah City for Atomic and Renewable Energy
(KACARE).
Around 70% of KSA’s buildings are not thermally insulated, according to Saudi Energy Efficiency
Centre (SEEC) [
5
], which is a major cause of the high electricity consumption. Consequently, a huge
part of air-conditioning loads is the heat transmission through the walls of buildings and roofs [
6
].
Another huge contributor to the high electricity consumption is occupant behavior. For instance, many
of the customers in KSA leave their air-conditioning units to run non-stop throughout the summer
months [
7
,
8
]. Over 73% of households in KSA turn on their air-conditioning systems from between
10 and 24h on a typical day during the summer months [
9
]. A significant amount of energy saving
can be achieved even by a small increase of AC thermostat settings, e.g., 1
◦
C or 2
◦
C [
10
,
11
]. These
savings are particularly important and desirable during the peak duration of electricity demands.

Energies 2019,12, 87 3 of 29
The setting of the thermostats to one value throughout the summer and to another value throughout
the winter (with the two values determined by averaging the daily optimal values) can significantly
reduce air-conditioning loads and increase energy savings (which could reach billions of dollars in cost
reduction at national level [12]) while still achieving the desirable thermal-comfort for occupants [6].
This paper will therefore investigate some solutions to the problem of KSA’s high-energy demand
associated with air-conditioning needs in residential building. It is clear that improvements in this area
will effectively encourage the reduced operation of air-conditioning units and will achieve savings
in the total energy consumption of buildings. These savings are particularly important during the
peak duration of electricity demand in the summer months. Typical residential buildings of KSA
will therefore be modelled and simulations will be carried out in order to understand the different
solutions, particularly with regard to ACs that can effectively reduce the energy consumption of
air-conditioning units. In addition, we examine the potential impacts of the achievable electricity
savings in each mode. Representative models describing occupancy behavior of KSA’s residential
buildings are not available, as far as the author is aware. These models are needed for computer
simulation of the energy consumption of KSA’s residential buildings in order to propose solutions that
will make energy savings achievable. In this respect, a survey is carried out as part of this study in
order to understand occupancy behavior times, in which rooms are occupied and electricity is being
used. To the author’s knowledge, this is the first attempt to describe KSA’s occupancy behavior of
residents. The insights gained from the survey will be used for the computer simulation of residential
buildings’ energy performance, which will further the understanding of possible energy savings if
AC systems are used in a more energy efficient manner. Thus, in addition to using data of the typical
house types and their actual dimensions and building materials, the computer simulation will take
into account the typical behavior of occupants of residential buildings in Riyadh city, making use of
behavioral data obtained through the survey that was carried out.
2. Literature Review
By reducing the energy usage of AC systems, a reduction in the consumption of energy can be
achieved. To accomplish this energy reduction, strategies can be modeled from past studies, such
as [
13
]: retrofitting existing AC systems; developing new AC systems; manufacturing designs, such
as increasing the number of condenser rows can lead to a higher Energy Efficiency Ratio (EER) in
KSA [
14
]; and using air-conditioning modes that are energy efficient (a suitable operational strategy
of AC could reduce approximately 23% of KSA’s energy consumption [
15
,
16
]). These strategies are
further elaborated upon in the ensuing paragraphs.
The Kingdom’s residential buildings have rapidly expanded in growth despite the fact that no
serious consideration of the energy efficiency of these buildings has been respected. Consequently,
the architectural design of the buildings encourages excessive energy consumption of AC systems.
Moreover, although the shape of the buildings unfavorably affects solar heat gain, the building
still lacks proper shading strategies [
9
,
17
]. The construction of climate-responsive, energy efficient,
and environmentally friendly building design technologies have recently been considered [
18
,
19
].
Retrofitting insulation to these buildings can be suggested to tackle this problem, but that would require
major and laborious work and would take many years to retrofit existing buildings. However, measures
for proper and better insulation could be setup, for example, improvements in the architectural design
for better energy efficiency in future residential building projects.
The behavior of occupants of a building is one of the four most important factors influencing the
building’s energy consumption. In particular, the building’s heat gains and the occupants’ comfort
requirements. The other three factors being the thermal control equipment installed in the building
(i.e., heating, ventilation, air-conditioning system, and hot-water heating system), the building’s
physical properties (i.e., orientation and location, as well as the buildings outdoor environment, such
as temperature and solar radiation) [
20
]. By far, the most difficult factor to model is occupancy behavior,
largely because humans, by their very nature, are unpredictable [
21
].The presence of humans within a

Energies 2019,12, 87 4 of 29
building initiates other activities, such as the emission of water vapor and carbon dioxide or the use
of electrical appliances for lighting, heating, or cooling, and so on. These variables affect the indoor
behavior of the building [
22
]. Importantly, humans adjust the appliances and/or their surroundings
in order to optimize their comfort (such as opening the windows, turning ON the ACs, or adjusting
the lighting).
The availability of occupancy information allows the prediction of occupancy pattern, which
is very important for scheduling climate control for the building [
23
]. The ability to predict both
short-term vacancies, such as intermittently entering and leaving a room or home, moving from one
room in a house to another, or leaving the house for work, and long-term vacancies, such as business
trips, illnesses, and holidays, can provide meaningful energy savings [
24
]. It is particularly difficult
to predict the occupancy behavior of an individual building, but trends of behavior can be studied
for a group of buildings in order to model long-term behavior [
20
]. In relation to KSA, none of the
studies carried out so far have dealt with customers daily electricity consumption pattern in residential
buildings. This information is crucial to understanding the occupancy behaviors that are critical
for scheduling and controlling the buildings’ climate [
23
]. Moreover, simulation results that can be
produced using building models and simulation software can only be as good as the ability of such
models to accurately predict occupancy behavior of the buildings [
22
]. For this paper, a survey has
been undertaken to investigate some of the behavioral factors causing high-energy consumption in
Saudi Arabia’s domestic buildings. The adjustment of consumer energy demands—especially when
considering customer behavioral changes as a result of education or financial incentives—is often
referred to as Demand Side Management (DSM). Through DSM, customers are encouraged to use less
energy during peak hours, which is often achieved by moving the time in which energy is used by
customers to off-peak times, such as early morning, evening, or weekends. In order to achieve DSM in
KSA, the use of thermal energy storage systems that will store energy for use by AC systems during
peak periods has been proposed in [
25
,
26
]. Through the analysis of the simulation results regarding
residential buildings equipped with thermal energy storage systems and of the historic electricity
demand data given in [
25
], promising potential of the technology can be observed. However, the lack
of efficiency in the production of ice during the day when electricity demand is at the peak limits the
capability of the technology.
Furthermore, Demand Response (DR) is a technology that enables an economic rationing system
through the adjustment of consumer energy consumption by using electric utilities to match consumer
demands with the energy supply. To achieve DR, electricity customers are offered low prices for
electricity during off-peak hours and a high price during peak periods. Studies have shown that
DR programs are capable of reducing the system peak by up to about 9% [
27
–
29
]. Examples of
the price-based DR programs are the real-time price (RTP), the critical-peak price (CPP), and the
time-of-use (ToU) programs. DR has been proposed for KSA recently (see [
30
]) as a way of managing
energy consumption. Moreover, the impact of the ToU pricing option for customers in KSA was
studied in [
31
], where it was observed that with ToU, the utilities were able to realize higher profits
from the residential sector and effectively reduce the sector’s high energy demand.
When extending DR to residential sector, the challenges include finding the optimum schedule
and the ICT infrastructure that can be deployed to actualize DR [
32
–
34
], as well as managing the
conflicting objectives of minimizing energy cost for the customer [
32
]. Some of the issues with DR are
customer inconvenience and responsiveness for different DR programs [
35
] and the effect of different
tariffs on the level of responsiveness [
36
]. Importantly, it has been shown that a large number of
customers do not respond to price by changing their consumption behavior. This factor must be
considered when designing DR schemes [
32
,
35
]. In addition, certain types of domestic appliances,
such as air-conditioning systems, are better candidates than others when providing DR [
36
]. Because
it is not practical that all customers respond simultaneously to take advantage of DR, it has been
suggested that as little as 5% of customers is enough to curtail electricity market price and as much as
20% of all customers can account for 80% of electricity price response [37].

Energies 2019,12, 87 5 of 29
In this paper, three modes of air-conditioning systems are proposed as solutions to mitigate the
challenges of KSA’s electricity demand during the peak period of summer. The first two modes that
will be discussed fall into the DSM category of electricity management strategy, while the third mode
of operation falls into the DR category.
3. Energy Use and Customer Behavior Survey
Developing a representative model of occupancy behavior for computer simulation and/or for
practical planning and design of buildings, in relation to air-conditioning systems, is important but
challenging, due to the lack of reliable data that covers a representative range of human behavior [
38
,
39
].
In this paper, a survey has been undertaken to investigate some of the behavioral factors causing
high-energy consumption in Saudi Arabia’s domestic buildings. A survey questionnaire was designed,
piloted, and distributed to people of different gender and regions across Saudi Arabia in order to obtain
information related to building design, occupants’ behavior with regard to electricity use, perception
on renewable energy, and so on.
Besides the online survey, interviews were carried out with several residences from two regions
in KSA. This was done to investigate these residencies behaviors regarding energy consumption,
particularly their AC use. Actual readings of household-level electricity consumption from the
different types of KSA housing units were measured using applications available on customer mobiles.
This could also provide an overview over the previous three years of consumption.
The questionnaire was divided into the following four sections: (1) A section investigating
accommodation data, such as the number of guest rooms, bedrooms, and bathrooms, as well as
accommodation types; (2) A section investigating the high electricity consumption of home appliances
by type and number; (3) A section investigating the occupants’ behavior regarding times rooms were
occupied and usage of high electricity consumption appliances; (4) A section exploring people’s
perceptions of renewable energy in homes.
Both English and Arabic languages were used in the questionnaire. The questionnaire of the
survey was distributed to participants from the whole kingdom via email, WhatsApp, and Twitter. The
online survey dissemination system was utilized because it was easier, faster, and less expensive than a
printed survey [
40
,
41
]. The questionnaire was initially sent through relatives, friends, colleagues, and
other acquaintances who further recruited additional acquaintances from their wide networks, such as
friends and colleagues [
42
]. Consequently, the sample includes various respondents who came from
all the regions of KSA. It is reasonable to assume that the survey has found the more common patterns
of room occupancy and AC use, which are taken forward into the simulation studies. The detailed
questionnaire is given in the Appendix A. In general, the questionnaire reached 451 participants; 383
of these completed and returned the forms. The demography of the respondents (i.e., family size,
number of bedrooms, guestrooms, and AC units in the household) is given in Figure 3.

Energies 2019,12, 87 6 of 29
Energies 2018, 11, x FOR PEER REVIEW 6 of 30
• The guest rooms are occupied mostly one day in a week.
• The main bedrooms of most respondents are occupied between 7:00 pm and 7:00 am the
following morning.
• 51% set their AC thermostats to between 18 °C and 20 °C, while 21% set it to between 21 °C and 23
°C.
• 66% rarely or never shut down their ACs during the summer months, compared to 22% that
usually do. The remaining 12% fall between these two extremes.
(a) (b)
(c) (d)
Figure 3. Demography of survey respondents: (a) Family members; (b) Number of bedrooms; (c)
Number of guest rooms; (d) Number of AC units.
(a) (b)
0
5
10
15
20
25
30
35
40
34567more
than 7
Percent %
Family members
Mean = 5.3
0
10
20
30
40
50
60
1 up to 3 4 up to 6 7 and more
Percent %
Number of AC units
0
10
20
30
40
50
60
70
123
Percent %
Numbers of Guest Rooms
0
10
20
30
40
50
60
1 up to 3 4 up to 6 7 and more
Percent %
Number of AC units
0
10
20
30
40
50
60
70
18_20 °C 21_23 °C 24_26 °C
Percent %
Thermostat setting
0
10
20
30
40
50
60
Never Rarely Sometimes Usually
Percent %
Remember to sutdown AC when leaving
Figure 3.
Demography of survey respondents: (
a
) Family members; (
b
) Number of bedrooms; (
c
)
Number of guest rooms; (d) Number of AC units.
Saudi Arabian culture features extended families living together in large houses, referred to as
villas; affluent families often own villas. The less affluent families often reside in individual houses that
are referred to as traditional houses. The rapid growth in KSA’s population required quick construction
of cheap accommodation in the form of several flats (apartments) within individual buildings (or
apartment blocks). Detached single- or two-story houses were also built with rapid construction.
In some cases, the two-storys were used as two separate floors (one floor on the top and the other
at the bottom). Regardless of whether a residential building is a villa, a traditional house, a flat, or
a single- or two-story house, the rooms were large; the average property in KSA is more spacious
than its equivalent in Western Europe. Therefore, from the survey, of the total number of respondents,
40% live in villas, 41% live in flats, and fewer than 20% live in traditional houses. Overall, 90% of the
respondents have more than one guest room (unlike in the English culture, where a guest room often
denotes a room within the house where guests are presented to sleep, a guest room in the Saudi culture
is more or less a reception room where guests are received and served; these rooms are typically larger
than bedrooms and, as custom, guests do not normally go beyond these guest rooms). Furthermore,
58% of respondents have two guest rooms and 33% have three guest rooms. Also, nearly 50% of
respondents have seven or more AC units at their homes.
The bar charts in Figure 4provide information on the household behavior in KSA and it is clear
that the behavior of AC users in KSA offers potential for substantial energy savings.

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• The guest rooms are occupied mostly one day in a week.
• The main bedrooms of most respondents are occupied between 7:00 pm and 7:00 am the
following morning.
• 51% set their AC thermostats to between 18 °C and 20 °C, while 21% set it to between 21 °C and 23
°C.
• 66% rarely or never shut down their ACs during the summer months, compared to 22% that
usually do. The remaining 12% fall between these two extremes.
(a) (b)
(c) (d)
Figure 3. Demography of survey respondents: (a) Family members; (b) Number of bedrooms; (c)
Number of guest rooms; (d) Number of AC units.
(a) (b)
0
5
10
15
20
25
30
35
40
34567more
than 7
Percent %
Family members
Mean = 5.3
0
10
20
30
40
50
60
1 up to 3 4 up to 6 7 and more
Percent %
Number of AC units
0
10
20
30
40
50
60
70
123
Percent %
Numbers of Guest Rooms
0
10
20
30
40
50
60
1 up to 3 4 up to 6 7 and more
Percent %
Number of AC units
0
10
20
30
40
50
60
70
18_20 °C 21_23 °C 24_26 °C
Percent %
Thermostat setting
0
10
20
30
40
50
60
Never Rarely Sometimes Usually
Percent %
Remember to sutdown AC when leaving
Energies 2018, 11, x FOR PEER REVIEW 7 of 30
(c) (d)
(e) (f)
(g) (h)
Figure 4. KSA’s household occupancy behavior: (a) AC thermostat setting; (b) Remember to
shutdown AC when leaving; (c) Male guest room occupancy day/week; (d) Male guest room
occupancy hours/day; (e) Lounge occupancy hours/day; (f) Bedrooms occupancy hours/day; (g)
Dining room occupancy hours/day; (h) Female guest room occupancy hours/day.
4. Parameters of the Energy Consumption Computer Simulation
The DesignBuilder® software was developed by DesignBuilder Software Ltd, based at Stroud,
Gloucestershire, United Kingdom. It is a popular and commercially-available software tool used for
modelling and simulating energy efficient and comfortable building designs. It has been selected
among others computer software tools, including DOE-2, EnergyPlus, TRNSYS, and ApacheSim for
simulation in this study, because it has an easy-to-use interface. Furthermore, it has useful built-in
features that facilitate energy performance comparisons, detailed modelling of Heating, Ventilation,
and Air Conditioning (HVAC) systems, and natural ventilation, which enable optimization of
0
10
20
30
40
50
60
1 d/w 2 d/w 3 d/w Most days
per week
Percent %
Male Guest rooms occupancy day/week
0
10
20
30
40
50
60
70
Percent %
Male Guest Rooms Occupancy hours/day
0
10
20
30
40
50
60
Percent %
Lounge Occupancy hours/day
0
10
20
30
40
50
60
70
7pm-7am 3pm-5am 10pm-7am
Percent %
Bedroom Occupancy hours/day
0
5
10
15
20
25
30
35
40
Percent %
Dining Occupancy hours/day
0
10
20
30
40
50
60
Percent %
Female Guest Rooms Occupancy hours/day
Figure 4.
KSA’s household occupancy behavior: (
a
) AC thermostat setting; (
b
) Remember to shutdown
AC when leaving; (
c
) Male guest room occupancy day/week; (
d
) Male guest room occupancy
hours/day; (
e
) Lounge occupancy hours/day; (
f
) Bedrooms occupancy hours/day; (
g
) Dining room
occupancy hours/day; (h) Female guest room occupancy hours/day.

Energies 2019,12, 87 8 of 29
Some highlights from these results include:
•The guest rooms are occupied mostly one day in a week.
•
The main bedrooms of most respondents are occupied between 7:00 pm and 7:00 am the
following morning.
•
51% set their AC thermostats to between 18
◦
C and 20
◦
C, while 21% set it to between 21
◦
C and
23 ◦C.
•
66% rarely or never shut down their ACs during the summer months, compared to 22% that
usually do. The remaining 12% fall between these two extremes.
4. Parameters of the Energy Consumption Computer Simulation
The DesignBuilder
®
software was developed by DesignBuilder Software Ltd, based at Stroud,
Gloucestershire, United Kingdom. It is a popular and commercially-available software tool used
for modelling and simulating energy efficient and comfortable building designs. It has been
selected among others computer software tools, including DOE-2, EnergyPlus, TRNSYS, and
ApacheSim for simulation in this study, because it has an easy-to-use interface. Furthermore, it
has useful built-in features that facilitate energy performance comparisons, detailed modelling of
Heating, Ventilation, and Air Conditioning (HVAC) systems, and natural ventilation, which enable
optimization of renewable energy systems for buildings’ energy performance improvements. It also
provides a simulation of cooling/heating design calculations over any period of time, such as a
day, a week, a season, or a year. In order to simulate the thermal performance of a house using the
DesignBuilder
®
software, the model parameters must be defined. Model parameters describe the
physical characteristics (including plan and geometry), installed equipment or appliances, building
purpose and occupancy behavior, the geographical location and climate, and the nature of the
surrounding environment, among others.
Characteristics of KSA residential buildings and construction materials are summarized as
follows [43,44]:
•
Cement-based hollow building blocks with a thickness of 15 cm or 20 cm and surface dimension
of 40 ×20 cm2.
•
Residential building walls consist of three layers: the external cement plaster, the hollow brick
(with different thickness sizes depending on whether they are inner or outer walls), and the
interior cement plaster.
Table 1shows the summary of other input data for the simulation software.
Table 1. Summary of the input data for simulation in DesignBuilder.
Lighting Openings HVAC DHW*Occupancy
•Luminous and
louvered ceiling.
Lighting is used with
following properties:
•Lighting energy =
0.4 W/m2
•Radiant
fraction = 0.37
•
Visible fraction = 0.18
•Single glazing
windows (6mm)
•Glass Area = 1m2
•Solar set-point
conduction
ratio = 1
•Position = Inside
•Split no fresh AC
Air with COP= 2.2
•Based in electricity
from grid
•DHW COP = 2.5
•Instantaneous
hot water
•density of 0.2
people/m2
•Metabolic
•Activity= Light
Manual Work
•Metabolic factor= 0.9
* Domestic Hot Water.
There are four general types of residential housing units in KSA: a floor in two-story house (which
shall be denoted as House 1 in this study), a traditional house (which shall be denoted as House 2
in this study), a villa, and a flat (or an apartment) [
45
]. According to KSA’s General Authority of
Statistics [
45
], there are 829,670 housing units in Riyadh city: 127,466 of House 1 type, 47,596 of House 2
type, 374,900 villas, and 279,708 flats; the average family size is 5.97. (Further details on the four groups

Energies 2019,12, 87 9 of 29
of Riyadh’s residential buildings are given in Table 2.) The occupants of these houses participated in
the customer behavior survey presented in the previous section. Moreover, the construction materials,
as well as the electricity bills, used in this study came from these houses. It is important to note that
the hourly electricity consumption data for individual dwellings in KSA was not available. However,
monthly consumption was taken and compared from at least two houses from each group due to the
availability of the application on customers’ mobiles; the previous three years consumption can also
be explored through this application. This setup will enable the comparison and validation of the
simulation results that will be presented. The plans and geometric descriptions of House 1, House 2,
the villa, and the flat are given in Figures 5–8, respectively.
Table 2. Details of the single floor house, the Villa, and the Flat.
Building Typology Building’s Name No. of Floors Area m2No of Occupants
A floor in two-story House 1 1 177.69
4 adult + 3 children
Traditional House House 2 1 171.82
2 adult + 4 children
Villa Villa 3 279.72
2 adult + 5 children
Apartment Flat 1 102
2 adult + 3 children
Energies 2018, 11, x FOR PEER REVIEW 9 of 30
.
Table 2. Details of the single floor house, the Villa, and the Flat.
Building Typology Building’s Name No. of Floors Area m
2
No of Occupants
A floor in two-story House 1 1 177.69 4 adult + 3 children
Traditional House House 2 1 171.82 2 adult + 4 children
Villa Villa 3 279.72 2 adult + 5 children
Apartment Flat 1 102 2 adult + 3 children
Figure 5. The plan and geometric description of House 1.
Figure 6. The plan and geometric description of House 2.
Figure 7. The plan and geometric description of Villa.
Figure 5. The plan and geometric description of House 1.
Energies 2018, 11, x FOR PEER REVIEW 9 of 30
.
Table 2. Details of the single floor house, the Villa, and the Flat.
Building Typology Building’s Name No. of Floors Area m
2
No of Occupants
A floor in two-story House 1 1 177.69 4 adult + 3 children
Traditional House House 2 1 171.82 2 adult + 4 children
Villa Villa 3 279.72 2 adult + 5 children
Apartment Flat 1 102 2 adult + 3 children
Figure 5. The plan and geometric description of House 1.
Figure 6. The plan and geometric description of House 2.
Figure 7. The plan and geometric description of Villa.
Figure 6. The plan and geometric description of House 2.

Energies 2019,12, 87 10 of 29
Energies 2018, 11, x FOR PEER REVIEW 9 of 30
.
Table 2. Details of the single floor house, the Villa, and the Flat.
Building Typology Building’s Name No. of Floors Area m
2
No of Occupants
A floor in two-story House 1 1 177.69 4 adult + 3 children
Traditional House House 2 1 171.82 2 adult + 4 children
Villa Villa 3 279.72 2 adult + 5 children
Apartment Flat 1 102 2 adult + 3 children
Figure 5. The plan and geometric description of House 1.
Figure 6. The plan and geometric description of House 2.
Figure 7. The plan and geometric description of Villa.
Figure 7. The plan and geometric description of Villa.
Energies 2018, 11, x FOR PEER REVIEW 10 of 30
Figure 8. The plan and geometric description of the flat.
5. Methodology
This study used a simulation-based methodology, as it allowed for the simulation of different
AC control schemes. Moreover, this approach allowed us to focus on many schemes at once, without
the cost of purchasing equipment and sensors, and without the setup time of different testing
conditions, which would have been necessary if an experimental approach had been considered. In
order to simulate the thermal performance of a house, the model had to be defined. It describes the
physical characteristics (including plan and geometry), installed equipment or appliances, building
purpose and occupancy behavior, the geographical location and climate, and the nature of the
surrounding environment, among others.
Despite the benefits and the progress made in the development of simulation software capable
of modelling complex building systems and their environments, computer-based simulations are not
without their disadvantages, which include the following [46,47]:
• Not all simulation data and parameters may be known or fully anticipated at the initial stage of
the simulation, i.e., introducing uncertainties and/or risk factors in the model.
• The number of input parameters for obtaining a model that will be suitable for simulation can
be very large. Therefore, a good calibration technique is important to obtain useful results.
The following steps outline the procedure adopted for carrying out the computer simulation in
this study:
5.1. Step 1: The Selection of Representative Buildings from a Group of Buildings
In this step, we chose the set of buildings described above, which represented the majority of
residential buildings in KSA (Riyadh, in particular).
5.2. Step 2: The Definition of the Buildings’ Geometrical Parameters
In this step, the geometry of the buildings was defined based on physical visits, interviews, and
surveys. The geometry was described in terms of room size (i.e., main rooms, guest rooms, kitchen,
toilets, and so on) and the dimensions of the openings (i.e., doors and windows).
5.3. Step 3: Construction Materials (Thermal and Physical Characteristics)
This step examined the construction materials and properties of the houses, such as the materials
for the external walls, internal walls, roofs, other parts of the buildings’ envelopes, the shading, the
glazing etc.
5.4. Step 4: Energy Consumption Profile for the Building Models
Figure 8. The plan and geometric description of the flat.
5. Methodology
This study used a simulation-based methodology, as it allowed for the simulation of different AC
control schemes. Moreover, this approach allowed us to focus on many schemes at once, without the
cost of purchasing equipment and sensors, and without the setup time of different testing conditions,
which would have been necessary if an experimental approach had been considered. In order to
simulate the thermal performance of a house, the model had to be defined. It describes the physical
characteristics (including plan and geometry), installed equipment or appliances, building purpose
and occupancy behavior, the geographical location and climate, and the nature of the surrounding
environment, among others.
Despite the benefits and the progress made in the development of simulation software capable of
modelling complex building systems and their environments, computer-based simulations are not
without their disadvantages, which include the following [46,47]:
•
Not all simulation data and parameters may be known or fully anticipated at the initial stage of
the simulation, i.e., introducing uncertainties and/or risk factors in the model.
•
The number of input parameters for obtaining a model that will be suitable for simulation can be
very large. Therefore, a good calibration technique is important to obtain useful results.
The following steps outline the procedure adopted for carrying out the computer simulation in
this study:
Step 1: The Selection of Representative Buildings from a Group of Buildings
In this step, we chose the set of buildings described above, which represented the majority of
residential buildings in KSA (Riyadh, in particular).
Step 2: The Definition of the Buildings’ Geometrical Parameters

Energies 2019,12, 87 11 of 29
In this step, the geometry of the buildings was defined based on physical visits, interviews, and
surveys. The geometry was described in terms of room size (i.e., main rooms, guest rooms, kitchen,
toilets, and so on) and the dimensions of the openings (i.e., doors and windows).
Step 3: Construction Materials (Thermal and Physical Characteristics)
This step examined the construction materials and properties of the houses, such as the materials
for the external walls, internal walls, roofs, other parts of the buildings’ envelopes, the shading, the
glazing etc.
Step 4: Energy Consumption Profile for the Building Models
This step defined the results obtained from the survey, specifically regarding energy consumption
and occupants’ behavior, including their activities and schedules in each room in relation to electricity
appliances usage, and, importantly, customer behavior in relation the usage of air-conditioning systems.
In addition, details regarding the numbers, types, and AC operational strategies are provided.
Step 5: Constructing the Models in DesignBuilder
This involves creating models of the various houses using the DesignBuilder software tool.
Modelling largely involves defining the model parameters, which we obtained from the previous
steps, to the software tool. The defined model parameters includes the location (e.g., longitude and
latitude), the orientation of the house, the climate condition (i.e., temperature and sun path), the
house construction layout (i.e., details of the dimensions of external walls, roofs, internal partitions,
floors, types of plasters, number of brick layers, etc.), the openings (i.e., glazing, shading, doors, and
vents), the lighting (i.e., types and typical operation schedule), the HVAC system (i.e., their types, sizes,
settings, and operation schedule), and the activities (i.e., schedules for occupancy), among others.
Step 6: Model Simulation and Thermostat Setting (Parameter) Discovery
The constructed models are used to simulate the performance of real KSA buildings. The results
are then analyzed and used to adjust the model parameters in order to obtain a reasonable correlation
between the actual (observed) energy consumption of the residential buildings and the predicted
(simulated) energy consumption. In other words, the modelling, simulation, and analysis cycle is an
iterative process. In particular, in order to ensure that the behavior of the models that is built with
DesignBuilder reflects the actual behavior of the houses, the thermostat settings of the air-conditioning
systems of the models have been adjusted to be consistent with the behavior survey. The monthly
electricity consumption values obtained by simulation agrees with reasonable accuracy those on the
actual electricity bills of the actual houses. The continuous operation, which a large number of people
fall into according to the survey, will be used as a baseline for comparison among the following three
proposed modes of AC operation.
Mode 1: The Scheduled Mode
In this mode, in order to improve indoor comfort, the air-conditioning systems are programmed
to turn on automatically an hour before the occupants are predicted to arrive home and to switch off
automatically an hour after it is un-occupied. It is assumed that the houses are equipped with sensors
that are able to detect occupancy and the occupancy activities are logged on a periodic basis so that
improvement can be made on the scheduling program in the next or future cycle of air-conditioning
operations. The unique feature of this mode is that the room temperature set points of the AC
thermostats are fixed to the value in which most people fix their thermostat settings, according to the
survey carried out in this research study (the fixed value is also backed up with the simulation carried
out using Step 6).
Mode 2: The Advanced Control Mode
The advanced control mode of the air-conditioning system operation has all the features of Mode
1 (the scheduled mode), except that here there is the added feature of changing the room temperature,
i.e., changing the setting of a thermostat from a lower temperature value when the room is occupied
to higher value when a room in the house is not occupied for a short duration, say, less than 10
minutes. Then, the advanced control mode can switch off the AC if the room remains unoccupied
for a reasonable time, say 1 h. The additional feature in this mode is motivated by the need to save

Energies 2019,12, 87 12 of 29
energy. The fact that a significant amount of energy savings can be achieved by even a small increase
of thermostat settings—e.g., 1
◦
C or 2
◦
C [
10
,
11
]—has been largely ignored in practice, but this fact is
important for this mode. Furthermore, this mode is in line with the electricity policies in KSA, which
recommends that householders set the thermostats of their ACs to 24
◦
C (instead of lower values), as
there is no significant difference between this thermostat setting and those of lower values to home
occupiers’ comfort [48].
Mode 3: The Remote-Control Mode
The remote-control mode is significantly different from the previous two modes mentioned in
that it is a Demand Response (DR) energy management approach. In this mode, the room temperature
set points are remotely controlled by the utility company during the peak time. For example, ACs
that are turned on in the subscribing customers’ residences are remotely set to a higher value of 24
◦
C
during peak times by the utility companies.
Step 7: Simulate the Final Models with Different Modes of Air-Conditioning Operation and
Compare with the Baseline Models from Step 6
6. Results
Having defined the parameters of the house types to the DesignBuilder software, Figure 9shows
the actual reading and simulated results of an air-conditioning system operating on the continuous
mode for a single floor in a two-story building (House 1). For the simulation results, two thermostat
settings were tested; DesignBuilder was used to simulate House 1 with a thermostat setting of 18
◦
C
and it was also used to simulate the house with a thermostat setting of 20
◦
C. As previously stated, 18
◦
C and 20
◦
C was the range that 51% of the survey respondents said they set their thermostat to during
the summer months. From the results of the simulation in Figure 9, it is clear that a 20
◦
C setting
is closer to the actual measured value that was actually taken for House 1 during this study. Thus,
the value of 20
◦
C will be taken as the typical value of thermostat settings in subsequent simulations.
Furthermore, it is important to note that the computer simulation was able to capture a significant
increase in energy consumption from May to September, which is primarily due to the use of the AC
during this period. Figure 10 shows the correlation plot of the two variables: the energy consumption
obtained using the simulation and the energy consumption obtained by direct measurement. The
value of 0.92 of the statistical coefficient of determination, R2, for the correlation plot indicates that the
predictions from simulation highly fit the data from the actual measurement.
Energies 2018, 11, x FOR PEER REVIEW 12 of 30
there is no significant difference between this thermostat setting and those of lower values to home
occupiers’ comfort [48].
5.6.1. Mode 3: The Remote-Control Mode
The remote-control mode is significantly different from the previous two modes mentioned in
that it is a Demand Response (DR) energy management approach. In this mode, the room
temperature set points are remotely controlled by the utility company during the peak time. For
example, ACs that are turned on in the subscribing customers’ residences are remotely set to a higher
value of 24 °C during peak times by the utility companies.
5.6. Step 7: Simulate the Final Models with Different Modes of Air-Conditioning Operation and Compare
with the Baseline Models from Step 6
6. Results
Having defined the parameters of the house types to the DesignBuilder software, Figure 9 shows
the actual reading and simulated results of an air-conditioning system operating on the continuous
mode for a single floor in a two-story building (House 1). For the simulation results, two thermostat
settings were tested; DesignBuilder was used to simulate House 1 with a thermostat setting of 18 °C
and it was also used to simulate the house with a thermostat setting of 20 °C. As previously stated,
18 °C and 20 °C was the range that 51% of the survey respondents said they set their thermostat to
during the summer months. From the results of the simulation in Figure 9, it is clear that a 20 °C
setting is closer to the actual measured value that was actually taken for House 1 during this study.
Thus, the value of 20 °C will be taken as the typical value of thermostat settings in subsequent
simulations. Furthermore, it is important to note that the computer simulation was able to capture a
significant increase in energy consumption from May to September, which is primarily due to the use
of the AC during this period. Figure 10 shows the correlation plot of the two variables: the energy
consumption obtained using the simulation and the energy consumption obtained by direct
measurement. The value of 0.92 of the statistical coefficient of determination, R2, for the correlation
plot indicates that the predictions from simulation highly fit the data from the actual measurement.
Figure 9. The actual reading and simulation results of the energy consumption of the air-conditioning
(AC) with thermostat settings of 18 °C and 20 °C.
0
1,000
2,000
3,000
4,000
5,000
6,000
Jan. Feb. March Ap. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Energy Consumption in kWh
Actual (kWh) Simulatin at 20°C Simulation at 18°C
Figure 9.
The actual reading and simulation results of the energy consumption of the air-conditioning
(AC) with thermostat settings of 18 ◦C and 20 ◦C.

Energies 2019,12, 87 13 of 29
Energies 2018, 11, x FOR PEER REVIEW 13 of 30
Figure 10. The correlation plot of the two variables: the energy consumption obtained using the
simulation with the AC working on the continuous mode at a thermostat setting of 20 °C and by using
the direct measurement obtained from the monthly bills.
The daily electricity profile for each simulated house showed an early afternoon peak, as seen in
the national aggregated data. However, precise calibration was not possible because of the lack of
hourly electricity consumption for the actual houses. Figure 11 shows the simulation of the hourly
electricity consumption of House 1 on a typical day during the summer (4 July).
To give an idea of how much energy consumption savings is possible, Figure 12 shows a
comparison of the actual readings, as well as the results of the simulation, of a thermostat setting at
20 °C in a villa working on continuous mode, Mode 1 (scheduled mode), and Mode 2 (advanced
control mode). For Mode 2, the thermostat settings for the AC are the same as Mode 1, except that
the ACs’ settings are changed from 20 °C to 24 °C, where it is supposed to be turned on and the room
is unoccupied. The results show that Mode 2 has a minimum energy consumption, followed by Mode
1. The simulation of the continuous mode closely matches what was obtained from the measurement
(actual household bill) for House 1.
Figure 11. The simulation of electricity consumption of House 1 on a typical summer day (4 July)
under the continuous mode.
R² = 0.9115
0
1,000
2,000
3,000
4,000
5,000
6,000
0 1,000 2,000 3,000 4,000 5,000 6,000
Simulation Energy Consumptionn
kWh
Actual energy Consumption kWh
Figure 10.
The correlation plot of the two variables: the energy consumption obtained using the
simulation with the AC working on the continuous mode at a thermostat setting of 20
◦
C and by using
the direct measurement obtained from the monthly bills.
The daily electricity profile for each simulated house showed an early afternoon peak, as seen
in the national aggregated data. However, precise calibration was not possible because of the lack of
hourly electricity consumption for the actual houses. Figure 11 shows the simulation of the hourly
electricity consumption of House 1 on a typical day during the summer (4 July).
Energies 2018, 11, x FOR PEER REVIEW 13 of 30
Figure 10. The correlation plot of the two variables: the energy consumption obtained using the
simulation with the AC working on the continuous mode at a thermostat setting of 20 °C and by using
the direct measurement obtained from the monthly bills.
The daily electricity profile for each simulated house showed an early afternoon peak, as seen in
the national aggregated data. However, precise calibration was not possible because of the lack of
hourly electricity consumption for the actual houses. Figure 11 shows the simulation of the hourly
electricity consumption of House 1 on a typical day during the summer (4 July).
To give an idea of how much energy consumption savings is possible, Figure 12 shows a
comparison of the actual readings, as well as the results of the simulation, of a thermostat setting at
20 °C in a villa working on continuous mode, Mode 1 (scheduled mode), and Mode 2 (advanced
control mode). For Mode 2, the thermostat settings for the AC are the same as Mode 1, except that
the ACs’ settings are changed from 20 °C to 24 °C, where it is supposed to be turned on and the room
is unoccupied. The results show that Mode 2 has a minimum energy consumption, followed by Mode
1. The simulation of the continuous mode closely matches what was obtained from the measurement
(actual household bill) for House 1.
Figure 11. The simulation of electricity consumption of House 1 on a typical summer day (4 July)
under the continuous mode.
R² = 0.9115
0
1,000
2,000
3,000
4,000
5,000
6,000
0 1,000 2,000 3,000 4,000 5,000 6,000
Simulation Energy Consumptionn
kWh
Actual energy Consumption kWh
Figure 11.
The simulation of electricity consumption of House 1 on a typical summer day (4 July)
under the continuous mode.
To give an idea of how much energy consumption savings is possible, Figure 12 shows a
comparison of the actual readings, as well as the results of the simulation, of a thermostat setting
at 20
◦
C in a villa working on continuous mode, Mode 1 (scheduled mode), and Mode 2 (advanced
control mode). For Mode 2, the thermostat settings for the AC are the same as Mode 1, except that the
ACs’ settings are changed from 20
◦
C to 24
◦
C, where it is supposed to be turned on and the room is
unoccupied. The results show that Mode 2 has a minimum energy consumption, followed by Mode 1.
The simulation of the continuous mode closely matches what was obtained from the measurement
(actual household bill) for House 1.

Energies 2019,12, 87 14 of 29
Energies 2018, 11, x FOR PEER REVIEW 14 of 30
Figure 12. Actual readings and simulation results of energy consumption with thermostat setting to
20 °C under different AC operation modes for the villa.
Table 3 and Figure 13(a) show the results of the annual energy consumption simulation,
including the energy savings over one year. Table 4 and Figure 13(b) indicated the results of the five
summer months. The simulation results of the continuous air-conditioning system operation and
Mode 1 and Mode 2 air-conditioning system operations for House 2, the villa, and the flat are given
in Tables 5–10, respectively.
Table 3. Annual energy consumption under the continuous and different modes of AC operation for
House 1.
AC system Operation Method Annual Consumption (kWh) Saving (kWh) Annual Saving (%)
Continuous at 20 °C 29,661 0 0
Continuous at 21 °C 28,770 891 3%
Mode 1 24,430 5231 18%
Mode 2 22,598 7063 24%
Mode 3 26,417 3244 11%
Table 4. Monthly energy consumption under the continuous and different modes of AC operation for
House 1 during the summer.
Months Continue
(kWh)
Mode1
(kWh)
Mode2
(kWh)
Mode 3
(kWh)
Mode 1
Saving (%)
Mode 2
Saving (%)
Mode 3
Saving (%)
May 3538 2922 2605 3913 17% 26% 16%
Jun. 4590 3423 3046 3695 25% 34% 19%
Jul. 5028 3661 3259 4023 27% 35% 20%
Aug. 5046 3696 3292 4047 27% 35% 19.%
Sep. 3757 3028 2695 2998 19% 28% 20%
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Energy Consumption in kWh
Actual (kWh) Simulation Mode 1 Mode2
Figure 12.
Actual readings and simulation results of energy consumption with thermostat setting to 20
◦C under different AC operation modes for the villa.
Table 3and Figure 13a show the results of the annual energy consumption simulation, including
the energy savings over one year. Table 4and Figure 13b indicated the results of the five summer
months. The simulation results of the continuous air-conditioning system operation and Mode 1
and Mode 2 air-conditioning system operations for House 2, the villa, and the flat are given in
Tables 5–10, respectively.
Table 3.
Annual energy consumption under the continuous and different modes of AC operation for
House 1.
AC system Operation Method Annual Consumption (kWh) Saving (kWh) Annual Saving (%)
Continuous at 20 ◦C 29,661 0 0
Continuous at 21 ◦C 28,770 891 3%
Mode 1 24,430 5231 18%
Mode 2 22,598 7063 24%
Mode 3 26,417 3244 11%
Energies 2018, 11, x FOR PEER REVIEW 15 of 30
(a)
(b)
Figure 13. Simulation results of energy consumption at 20 °C thermostat setting under continuous
mode, Mode 1 and Mode 2 operation of the air-conditioning systems for House 1: (a) Annual
Consumption; (b) Consumption during the summer months.
Table 5. Annual energy consumption under the continuous and different modes of AC operation for
House 2.
AC system Operation Method Consumption (kWh) Saving (kWh) Annual Saving (%)
Continuous at 20 °C 39,355 0 0
Continuous at 21 °C 38,291 1,064 3%
Mode 1 27,314 1,2040 31%
Mode 2 24,885 1,4469 37%
Mode 3 34,989 4,366 11%
Table 6. Monthly energy consumption under the continuous and different modes of AC operation for
House 2 during the summer.
Month Simulation
(kWh)
Mode 1
(kWh)
Mode 2
(kWh)
Mode 3
(kWh)
Mode 1
Saving (%)
Mode 2
Saving (%)
Mode 3
Saving (%)
May 5,918 3,974 3,537 4,853 33% 40% 18%
Jun. 6,974 4,496 4,002 5,614 36% 43% 19%
Jul. 7,320 4,849 4,316 5,856 34% 41% 20.%
Aug. 7,568 4,675 4,161 6,069 38% 45% 19%
Sep 5,036 6,343 4,087 4,089 36% 42% 18%
Table 7. Annual energy consumption under the continuous and different modes of AC operation for
the Villa.
AC Operation Method Consumption (kWh) Saving (kWh) Annual saving (%)
Continuous 67,277 0 0
Mode 1 57,368 9,908 15%
Mode 2 52,786 14,490 22%
Mode 3 61,219 6,058 9%
18% 24%
11%
0
5,000
10,000
15,000
20,000
25,000
30,000
Continuous
at 20°C
Mode 1 Mode 2 Mode 3
Energy kWh
Consumption Annual saving
0
1,000
2,000
3,000
4,000
5,000
May Jun. Jul. Aug. Sep.
Energy Consumption in kWh
Continuous (kWh) Mode 1 Mode 2
Figure 13.
Simulation results of energy consumption at 20
◦
C thermostat setting under continuous
mode, Mode 1 and Mode 2 operation of the air-conditioning systems for House 1: (
a
) Annual
Consumption; (b) Consumption during the summer months.

Energies 2019,12, 87 15 of 29
Table 4.
Monthly energy consumption under the continuous and different modes of AC operation for
House 1 during the summer.
Months Continue
(kWh)
Mode1
(kWh)
Mode2
(kWh)
Mode 3
(kWh)
Mode 1
Saving (%)
Mode 2
Saving (%)
Mode 3
Saving (%)
May 3538 2922 2605 3913 17% 26% 16%
Jun. 4590 3423 3046 3695 25% 34% 19%
Jul. 5028 3661 3259 4023 27% 35% 20%
Aug. 5046 3696 3292 4047 27% 35% 19.%
Sep. 3757 3028 2695 2998 19% 28% 20%
Table 5.
Annual energy consumption under the continuous and different modes of AC operation for
House 2.
AC system Operation Method Consumption (kWh) Saving (kWh) Annual Saving (%)
Continuous at 20 ◦C 39,355 0 0
Continuous at 21 ◦C 38,291 1,064 3%
Mode 1 27,314 1,2040 31%
Mode 2 24,885 1,4469 37%
Mode 3 34,989 4,366 11%
Table 6.
Monthly energy consumption under the continuous and different modes of AC operation for
House 2 during the summer.
Month Simulation
(kWh)
Mode 1
(kWh)
Mode 2
(kWh)
Mode 3
(kWh)
Mode 1
Saving (%)
Mode 2
Saving (%)
Mode 3
Saving (%)
May 5,918 3,974 3,537 4,853 33% 40% 18%
Jun. 6,974 4,496 4,002 5,614 36% 43% 19%
Jul. 7,320 4,849 4,316 5,856 34% 41% 20.%
Aug. 7,568 4,675 4,161 6,069 38% 45% 19%
Sep 5,036 6,343 4,087 4,089 36% 42% 18%
Table 7.
Annual energy consumption under the continuous and different modes of AC operation for
the Villa.
AC Operation Method Consumption (kWh) Saving (kWh) Annual Saving (%)
Continuous 67,277 0 0
Mode 1 57,368 9,908 15%
Mode 2 52,786 14,490 22%
Mode 3 61,219 6,058 9%
Table 8.
Monthly energy consumption under the continuous and different modes of AC operation for
Villa during the summer.
Month Simulation
(kWh)
Mode 1
(kWh)
Mode 2
(kWh)
Mode 3
(kWh)
Mode 1
Saving (%)
Mode 2
Saving (%)
Mode 3
Saving (%)
May 7,647 5,879 4,997 6,347 23% 35% 17%
Jun. 8,608 6,585 5,597 7,016 24% 35% 18%
Jul. 9,035 6,905 5,870 7,246 24% 35% 20%
Aug. 9,315 7,124 6,056 7,452 24% 35% 20%
Sep. 7,879 6,083 5,474 6,240 23% 31% 21%

Energies 2019,12, 87 16 of 29
Table 9.
Annual energy consumption under the continuous and different modes of AC operation for
the Flat.
AC Operation Method Consumption (kWh) Saving (kWh) Annual Saving (%)
Continuous 25,180 0 0
Mode 1 19,028 6,152 24%
Mode 2 18,168 7,012 28%
Mode 3 21,759 3,421 14%
Table 10.
Monthly energy consumption under the continuous and different modes of AC operation for
Flat during the summer.
Month Simulation
(kWh)
Mode 1
(kWh)
Mode 2
(kWh)
Mode 3
(kWh)
Mode 1
Saving (%)
Mode 2
Saving (%)
Mode 3
Saving (%)
May 3,530 2,362 2,128 2,852 33% 40% 19%
Jun. 3,713 2,492 2,280 2,896 33% 39% 22%
Jul. 3,909 2,620 2,490 3,010 33% 36% 23%
Aug. 3,942 2,640 2,479 3,000 33% 37% 23%
Sep. 3,544 2,372 2,249 2,694 33% 37% 20%
7. Discussion
We analyzed the simulation results for the annual electricity consumption and saving for each
studied building. The results of the preceding section can be summarized as follows:
•
For House 1, House 2, the villa, and the flat, the yearly savings achieved through Mode 1 operation
mode were 18%, 31%, 15%, and 25%, respectively.
•
For House 1, House 2, the villa, and the flat, the yearly savings achieved through Mode 2 operation
mode were 24%, 37%, 22%, and 29%, respectively.
•
For House 1, House 2, the villa, and the flat, the yearly savings achieved through Mode 3 operation
mode were 11%, 11%, 9%, and 13%, respectively.
Herein, only the annual savings have been given. For the five summer months, the savings ranges
from 16% to 35% for House 1, 18% to 45% for House 2, 17% to 35% for the villa, and 19% to 40% for the
flat, depending on the ACs mode of operation.
Table 11 shows the corresponding savings for each house type category and the total savings
for the city during the peak hour of 2:00 pm–3:00 pm. Accordingly, the total power needed for the
whole of Riyadh during the peak-time hour is 8792 MW, which represents 43% of Riyadh during peak
power demand; our data compares well with the residential portion (50% of total demand (20,329
MW)) as indicated in [
4
]. By applying the different AC operation modes that we proposed, the values
of total power savings for the whole of Riyadh were 5026, 5608, and 1280 MW for Modes 1, 2, and 3,
respectively, during peak hours. The results are summarized in Table 12.

Energies 2019,12, 87 17 of 29
Table 11.
The total peak power and savings for each house on different AC operations modes for the
whole of Riyadh region.
Dwelling’s
Name AC Mode Peak in (kW) Number of
Dwelling
Total Consumption
(kW)
Total Saving
(kW)
House 1
Continuous 6.3 127,466 803,036 0
Mode 1 3.4 127,466 433,384 369,651
Mode 2 2.85 127,466 363,278 439,758
Mode 3 5.22 127,466 665,373 1,376,638
House 2
Continuous 7.5 47,596 356,970 0
Mode 1 5.9 47,596 280,816 76,154
Mode 2 3.6 47,596 171,346 185,624
Mode 3 5.8 47,596 276,057 80,913
Villa
Continuous 17 374,900 6373,300 0
Mode 1 6.2 374,900 2324,380 4,048,920
Mode 2 5.65 374,900 2118,185 425,511
Mode 3 14.61 374,900 5477,289 896,011
Flat
Continuous 4.5 279,708 1,258,686 0
Mode 1 2.6 279,708 727,241 531,445
Mode 2 1.9 279,708 531,445 727,241
Mode 3 3.91 279,708 1,093,658 165,028
Table 12. The total peak power and saving of all houses types for the whole of Riyadh.
AC Operation Mode Total (MW) Saving (MW) Annual Saving (%)
Continuous Mode 8792 0 0
Mode 1 3766 5026 25%
Mode 2 3184 5608 28%
Mode 3 7512 1280 7%
From the results, it can be observed that the savings achieved with Mode 3 (the remote-control
mode) gave the lowest energy savings. The savings achieved with Mode 2 (the advanced control mode)
gave the best energy savings. For the individual houses, the results of Mode 2 operation mode was
superior to what was reported in the literature, that alleged it was possible to achieve up to 37% energy
savings using a fixed monthly optimum thermostat setting [
6
]. In this study, it was not the optimum
thermostat settings for each month that was used. Rather, it was the scheduling of the thermostat
setting (in the case of Mode 1 and Mode 2) and the use of DR (in the case of Mode 3) throughout
the summer months that led to better savings. In addition, another reason why the fixed monthly
optimum thermostat setting described in [
6
] is impractical is that it presumes that home occupants will
be willing to re-set their thermostat monthly to a new value. As was previously observed in the survey
results, up to 66% of the respondents rarely or never shut down their ACs during the summer months,
compared to 22% that usually do, while the remaining 12% fall between these two extremes. Moreover,
it is unrealistic to expect that enough number of residents will manually turn off their air-conditioning
systems [
49
]. This is confirmed by SEEC, which often send reminders and requests that people in
KSA set their thermostat to 24
◦
C; their call is mostly always ignored [
5
], as the low energy tariff
and the operative temperature may not provide comfort for occupants due to the high ambient
temperature and lack of thermal insulation in about 70% of the residential buildings [
8
]. Thus, Mode
1 and Mode 2 solutions allows the air-conditioning systems to run on a scheduled non-continuous
basis. Mode 3 uses the DR approach, where the utilities can automatically control the thermostat
settings of the air-conditioning systems of households, if and when appropriate. Consequently, the
proposed approaches do not require daily thought or actions from occupants to achieve energy savings.
Furthermore, these modes are realistic and practical as they do not require home occupiers’ intervention
to change the thermostat settings.

Energies 2019,12, 87 18 of 29
8. Limitation
The survey method undertaken in this study has indicated the range of domestic energy use
patterns. It cannot necessarily capture the full range of behaviors, in the absence of any more exhaustive
survey data.
The hourly electricity consumption data for individual dwellings in KSA was not available;
measurements of hourly energy consumption need to be undertaken as a future work.
Sensors are central to achieving the smart control of AC. However, to achieve the level of savings
obtained in this study, the residential buildings will require sensors capable of detecting occupancy
with a high degree of accuracy to obtain the schedule that can be periodically updated. Occupancy
sensor technologies are an area of on-going research and future work should focus on the development
of cheap, high accuracy occupancy sensors without the challenges of privacy and data intrusiveness.
Still, regarding AC Modes 1 and 2, the control system and associated sensors must be a standalone
system, without interference from the utility company in order to respect privacy.
The monthly bills for houses could be obtained through using applications available on customer
mobiles. This could also provide an overview of the previous three years of consumption. However,
it is difficult to get details regarding houses, such as geometry information, occupants’ behavior
pertaining to times of rooms occupied, and usage of households’ appliances, as most occupants deem
this information confidential.
9. Recommendations
A program to support evidence based policymaking should be a comprehensive nationwide
survey designed to obtain statistically valid data on energy use patterns. In order to achieve the
much-needed savings in energy consumption via AC systems in the residential sector, the electricity
policy makers and stakeholders could encourage both utility companies and residents to use AC smart
controls in the context of DSM. Additionally, utility companies could be given a period of time to
ensure that a percentage of their customers deploy AC systems equipped with smart scheduling and
controls in their house in order to increase their percentage gradually. Moreover, the companies that
provide these technologies could be supported through facilities, such as tax reduction or tax relief.
To reduce the AC consumption during the peak time, utility companies might encourage their
customers to reduce their energy by subscribing to a DR scheme to let the companies control some or
most of their AC units during the peak hours. The utility company could provide financial incentives
for the customers in the form of free hours in off-peak time or reduce the monthly bills by a particular
percent. To be ethical, these actions can only be based on the mutual agreement between the utility
companies and their customers [37,50,51].
10. Conclusions
In this paper, we presented potential solutions to the high energy demand associated with the
use of air-conditioning systems in KSA residential areas. The main idea of the proposed solutions is
that it is possible to reduce air-conditioning energy consumption through appropriate scheduling or
remote thermostat setting, while the comfort level of the building is set to an acceptable level. Using
the DesignBuilder
®
software, the results of the three modes of operation show that they can effectively
reduce energy consumption and, in the advance mode of operation (Mode 2), can achieve up to 30%
to 40% total annual energy savings, or 30% to 45% during the summer months, depending on the
house type. However, to achieve this level of saving, the computer simulation assumes that residential
buildings are equipped with sensors capable of detecting occupancy with a high degree of accuracy in
order to obtain the schedule that can be periodically updated.
Future work should look into other types of domestic appliances, in addition to AC systems, that
will be good DR candidates, in order to achieve an even larger amount of energy savings. Furthermore,
it would be worthwhile to investigate the energy savings achievable with other techniques the can

Energies 2019,12, 87 19 of 29
control AC systems based on occupancy behavior obtained from high accuracy occupancy sensors.
Artificial intelligence techniques that combine sensor information with other data, such as mobile
phone use, are an area of on-going research that could provide helpful assistance in this area. Moreover,
future work should investigate the extension of the modes of AC operations proposed for the residential
sector to government buildings, such as mosques and schools, and to other sectors. Mosques and
schools are particularly suitable because they are opened only during specific times of the day, making
them good candidates for AC system operation scheduling.
Author Contributions:
J.A., conceived of the presented idea, planned and carried out the experiments, developed
models and carried out simulations, analyzed and interpreted of the results, took the lead in writing and analysis
the manuscript. P.B., contributed to the final version of the manuscript and supervised the project.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflicts of interest.
Appendix A
Public Survey and Questionnaire
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Appendix A
Public Survey and Questionnaire

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