1
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Residential Energy Model for
Evaluating Energy Demand and
Energy Efciency Programs in
Saudi Residential Buildings
Mohammad Aldubyan, Moncef Krarti,
and Eric Williams
November 2020
Doi: 10.30573/KS--2020-MP05
2
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
About KAPSARC
The King Abdullah Petroleum Studies and Research Center (KAPSARC) is a
non-prot global institution dedicated to independent research into energy economics,
policy, technology and the environment across all types of energy. KAPSARC’s
mandate is to advance the understanding of energy challenges and opportunities
facing the world today and tomorrow, through unbiased, independent, and high-caliber
research for the benet of society. KAPSARC is located in Riyadh, Saudi Arabia.
This publication is also available in Arabic.
Legal Notice
© Copyright 2020 King Abdullah Petroleum Studies and Research Center (“KAPSARC”).
This Document (and any information, data or materials contained therein) (the
“Document”) shall not be used without the proper attribution to KAPSARC. The
Document shall not be reproduced, in whole or in part, without the written permission
of KAPSARC. KAPSARC makes no warranty, representation or undertaking whether
expressed or implied, nor does it assume any legal liability, whether direct or indirect,
or responsibility for the accuracy, completeness, or usefulness of any information that
is contained in the Document. Nothing in the Document constitutes or shall be implied to
constitute advice, recommendation or option. The views and opinions expressed in this
publication are those of the authors and do not necessarily reect the ofcial views or
position of KAPSARC.
3
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
The Residential Energy Model (REEM) simulates Saudi Arabia’s residential building stock,
disaggregated by building type, vintage, and location, using an engineering bottom-up approach.
It utilizes open-source data and building codes to estimate energy use intensity according to building
characteristics and climate.
REEM can assess total energy consumption and peak demand for each building type and vintage in
different climates, disaggregated by province or region.
The model can evaluate the impact of various energy efciency measures and demand-side
management on total consumption and peak demand, providing policymakers with useful insights for
designing energy efciency programs.
Key Points
4
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Summary
This paper describes the development of the
Residential Energy Model (REEM) for Saudi
Arabia using an engineering bottom-up
approach. The model can assess energy demand
for the current residential building stock and the
impact of energy efciency and demand-side
management programs. It accounts for the makeup
and features of the Kingdom’s existing housing
stock using 54 prototypes of residential buildings
dened by three building types, three vintages, and
six locations representing different climatic zones.
Using the resulting 54 prototypes, total demand can
be estimated on provincial, regional, and national
levels. The regional and national levels can then be
calibrated against actual reported data for energy
consumption associated with the country’s four
primary regions — central, east, west, and south
— for any year to adapt to future changes in the
housing stock.
The model utilizes open-source data provided by
the government and other sources to simulate the
entire building stock, disaggregated by the above
three characteristics. It then allows calibration
for unobserved qualities that affect electricity
consumption, including consumption patterns,
certain thermal characteristics, and appliance
usage. The calibrated model provides useful insights
on how electricity consumption and peak demand
vary with building type, vintage, and location. It also
disaggregates consumption by end use to determine
the highest contributors to energy demand.
After calibration, REEM can evaluate the economic
and environmental impacts of different energy
efciency retrot options at ‘micro’ and ‘macro’
levels, individually or in combination. The model
can also estimate the reduction in both energy
consumption and peak demand associated
with implementing specied retrots, again
disaggregated by building type, vintage, and
location. Thus, REEM can assist policymakers in
upgrading building codes and designing impactful
energy efciency programs for Saudi Arabia’s
residential sector.
5
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Introduction
Residential buildings dominate the demand
side of the electricity sector in Saudi Arabia.
From 2009 to 2018, homes accounted for
around 50% of total electricity consumption in the
Kingdom, far more than commercial or government
structures (SAMA 2019). Due to the hot climate,
air conditioning (AC) alone comprised 64% of
household electricity use as of 2019.
Until several years ago, residential electricity
consumption grew swiftly alongside the country’s
steadily expanding housing stock. From 2007 to
2018, household electricity use increased 45%,
driven by a 73% rise in housing units (SAMA 2019).
However, the two trends have since diverged.
Housing demand, especially from younger
segments of the population, has continued to climb,
to the extent that in early 2020, the Ministry of
Housing launched an initiative to develop 200 million
square meters of new residential projects around the
country (Al Riyadh 2020). Yet since 2016, electricity
consumption growth has decelerated signicantly
across all sectors, including residential buildings,
largely due to government efforts to reform the
energy sector and rationalize electricity markets.
These have resulted in higher consumer electricity
prices and increased energy efciency, depressing
demand.
Over the last two decades, the government
has gradually introduced stricter standards and
regulations to curb energy consumption in the
building sector. Since 2001, the authorities have
implemented and regularly updated minimum
energy performance standards (MEPs) for
refrigerators, freezers, washing machines, and air
conditioners (SASO 2012, 2013, 2014b, 2018a,
2018b, 2018c, 2018d). In 2010, the government
established the Saudi Energy Efciency Center
(SEEC), which “aims to rationalize and increase the
energy efciency in production and consumption
in order to preserve the [Kingdom of Saudi Arabia]
KSA natural resources and enhance the economic
and social welfare of KSA population” (SEEC 2020).
Regulators also adopted new thermal requirements
in 2014 to improve the energy performance of new
residential buildings. However, the energy efciency
of buildings in Saudi Arabia remains low, partly due
to the difculty of enforcing regulations (Alrashed
and Asif, 2014; Shenashen, Alshitawi, and Almasri
2016; Krarti, Dubey, and Howarth 2017).
Energy efciency retrots can decrease both fuel
consumption and the need for electricity generation
capacity, potentially making them very cost-effective
investments. Given their modest upfront costs and
high rates of return, targeted energy efciency
programs will often prove attractive for households
and businesses. Krarti, Dubey and Howarth (2017)
found that Saudi Arabia has signicant potential
for energy savings through energy efciency
requirements, not only in new construction, but
also through retrotting energy systems in existing
buildings. Their study estimates that in 2014, a
nationwide retrot program that included AC and
insulation for Saudi Arabia’s existing building stock
could have saved over 100 terawatthours per year
(TWh/year), or 25% of the Kingdom’s total electricity
consumption, and reduced its peak demand by 25
gigawatts (GW), or 27%.
This paper describes the methodological
underpinnings of the Residential Energy Model
(REEM) from the perspective of assessing the
economic and environmental benets of retrotting
Saudi Arabia’s existing residential building stock.
Our analysis considers the impacts of a wide range
of energy efciency measures (EEM) on both
energy consumption and peak demand, for 54
prototypes of residential buildings dened by type,
vintage, and location (climatic zone). The remainder
of the paper is organized as follows. The rst section
6
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
outlines the characteristics and energy performance
of the Kingdom’s existing housing stock, based on
reported data and surveys. The second section
describes REEM and walks through its calibration
using reported energy consumption data for the
Introduction
residential sector disaggregated by region. Finally,
the paper concludes by utilizing REEM to assess
Saudi Arabia’s residential building stock in 2018 to
illustrate the model’s output.
7
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Residential Building Stock
Characteristics and Data Collection
Constructing a bottom-up model for the
entire building stock of a country or region
can be complex due to the uncertainty
surrounding many parameters that shape energy
demand. Two of the most difcult aspects are the
physical characteristics of houses and the electricity
usage patterns of households, both of which require
granular data; these are necessary to simulate
the current stock and estimate baseline electricity
consumption. The General Authority for Statistics
(GaStat), a government agency responsible for
conducting surveys in Saudi Arabia, publishes
two annual reports from which REEM directly
incorporates data: the Housing Survey and the
Energy Household Survey (GaStat 2018a, 2018b).
These resources provide the number of housing
units, total cooled oor area, total heated oor
area, energy usage, number of appliances, and
other such information, disaggregated by building
type, vintage, and/or province. REEM utilizes the
GaStat data, in conjunction with other published
studies related to the Kingdom’s housing sector,
to create energy models for the 54 prototypes
of housing units according to type, vintage, and
location (climatic zone). These prototypes can be
used to estimate 54 different energy use intensities
(EUI) which can be then used to estimate the total
electricity consumption in the residential building
stock based on total livable areas of each building
type, with different vintages, in different locations, as
provided by GaStat.
After the model simulates the residential building
stock, it can be calibrated and veried against actual
electricity consumption data for the same year by
region (central, east, west, and south) from the
Electricity and Cogeneration Regulatory Authority
(ECRA). ECRA provides an annual report on the
electricity market in Saudi Arabia, which includes
electricity sales broken down by suppliers, and
electricity consumption by sector and geographical
region (ECRA 2018). The calibration mechanism
adjusts certain parameters for which data can
neither be found publicly nor directly estimated,
such as lighting intensity, the energy efciency of
certain equipment, and usage patterns.
8
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Description of the Residential Building
Stock Model
REEM Development
The literature commonly features two approaches
to predict energy demand and, hence, the energy
consumption of building stocks.
• Top-down modeling methods correlate
energy demand with other variables such
as climatic parameters (e.g., degree-days,
outdoor temperatures) and econometric factors
(e.g., energy prices, income levels). These
approaches rely heavily on historical datasets
and are widely used in macroeconomic analyses
(Edmonds, Wise and MacCracken 1994; Sailor
and Lu 2004; Kyle et al. 2010).
• Bottom-up frameworks utilize building model
prototypes representative of the building stock
to estimate energy consumption and end
uses. The outputs for the individual models
are added using statistical or engineering
analysis approaches to estimate the aggregated
values for the entire building stock (Swan and
Ugursal 2009; Kavgic et al. 2010; Oladokun and
Odesola 2015). The number of representative
building models varies widely depending on
the application and the diversity of building
stocks (Caputo, Costa, and Ferrari 2013; Davila,
Reinhart and Bemis 2016; Luddeni et al. 2018).
Figure 1. REEM framework for the residential building stock in Saudi Arabia.
Source: KAPSARC.
ECRA actual
consumption
by region
Calibration
REEM total
consumption
by region
Total
building
stock
consumption
Central
Villa
Location
(climatic
zone)
Province
Traditional
house
Apartment
Building type
Vintage
54 EUI prototypes
Total livable area
by building type
and vintage
Old
Recent
New
Riyadh
Hail
Qassim
Eastern Region
Jouf
Makkah
Madinah
Aseer
Albahah
Tabuk
Northern
borders
Jazan
Najran
Eastern
Western
Tabuk
Riyadh
Dhahran
Jeddah
Abha
Jazan
Central
Southern
Eastern
Western
Southern
9
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Table 1. Building construction specications for the three building types.
Description of the Residential Building Stock Model
REEM applies a bottom-up approach using
deterministic engineering analysis of 54
representative building prototypes to predict
electricity consumption and peak demand for the
residential building stock in Saudi Arabia. The model
can then evaluate the effectiveness of a wide range
of energy efciency programs targeting existing
buildings. Figure 1 illustrates the residential building
prototype framework used in this study.
This study considers three characteristics to
represent Saudi Arabia’s residential building stock:
building type, vintage, and location (climatic zone).
Building type: Housing units available in Saudi
Arabia comprise three primary types: villas,
apartment units, and traditional houses. We dene
their respective characteristics using data collected
from reported energy audit studies (Taleb and
Sharples 2011; Algarni and Nutter 2013; Alaidroos
and Krarti 2015). Table 1 summarizes the main
features of the housing types. The prototype energy
models have been calibrated as shown in Figure 2
based on the actual electricity consumption of these
three reported energy audit studies.
Building model Villa Apartment Traditional house
Number of oors 2 3 2
Total oor area 525 m21,260 m2232 m2
Wall construction 20 mm plaster outside + 150 mm concrete hollow block + 20 mm plaster inside
Roof construction 10 mm built-up roong + 200 mm concrete roof slab + 13 mm plaster inside
Floor construction Ceramic tile + 100 mm concrete slab on grade
Glazing Single-clear with wood frames
Window-to-wall ratio 13% 15% 15%
Inltration 0.8 ACH 0.8 ACH 0.8 ACH
Cooling set point 23°C 24°C 24°C
HVAC system Split DX AC window AC window
EER 7.5 8.5 8.5
Occupancy period 24-hour/day 24-hour/day 24-hour/day
Note: ACH = air change per hour; EER = energy efciency ratio; HVAC = heating, ventilation and air conditioning;
m2 = square meters; mm = millimeter
Sources: Taleb and Sharples 2011; Algarni and Nutter 2013; Alaidroos and Krarti 2015.
10
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Description of the Residential Building Stock Model
Figure 2. Calibration analysis results of housing prototype energy models.
Villa (compared to data from Alaidroos and Krarti [2015])
Apartment (compared to billed data from Taleb and Sharples [2011])
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
123456789101112
Monthly Electricity Consumption
(kWh/month)
Villa - Riyadh
Billed Consumption Model Prediction
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
1 2 3 4 5 6 7 8 9 10 11 12
Monthly Electricity Consumption
(kWh/month)
Apartment - Jeddah
Billed Consumption Model Prediction
11
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Description of the Residential Building Stock Model
Traditional house (compared to billed data from Algarni and Nutter [2013])
Vintage: Vintage reects a housing unit’s energy
consumption efciency. As of 2018, 33% of houses
in Saudi Arabia were less than 10 years old, but
only 20% were insulated (GaStat 2019). This is
partly because mandatory thermal insulation was
only implemented in 2010 (SEC 2020). The Saudi
Standards, Metrology and Quality Organization
(SASO) has been regularly updating the minimum
energy performance requirements for different
sectors, including buildings. Thus, a building’s
vintage, on average, reects its efciency in terms
of insulation and numerous other parameters, such
as window and roof specications. Moreover, older
buildings are generally in poorer condition and in
greater need of renovation (GaStat 2018). This study
denes three vintage classications.
• Model-N: new construction (typically less
than ve years old, includes wall insulation,
roof insulation, double-glazed windows)
• Model-R: recently built housing (between ve
and 10 years old, includes thermal insulation
in walls and roof, but single-glazed windows)
• Model-O: old buildings (over 10 years old, no
thermal insulation, single-glazed windows)
The annual GaStat Housing Survey provides data
for the age of housing units in Saudi Arabia by
province and dwelling type (GaStat 2018a).
Location: The energy performance of housing
depends heavily on its geographic location and
associated climatic conditions. Saudi Arabia
comprises 13 provinces, typically grouped into four
administrative regions (central, east, west, and
south). While SASO building-envelope thermal
requirements dene only three climatic zones for
the Kingdom, a study by Alrashed and Asif (2015)
proposed ve, represented by Jeddah, Riyadh,
Dhahran, Tabuk, and Abha. For greater precision,
this study incorporates climatic data for six different
locations (Jeddah, Riyadh, Dhahran, Abha, Jazan,
and Tabuk) that represent the range of weather
conditions in the country, as summarized in Table 2.
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
123456789101112
Monthly Electricity Consumption
(kWh/month)
Traditional House - Abha
Billed Consumption Model Prediction
12
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Description of the Residential Building Stock Model
Table 2. Geographical distributions, weather specications, and weather classications for Saudi Arabia.
Source(s): * Al-Hadhrami (2013).
Region Province Representative
city
Cooling
degree-days*
(oC-days/year)
Heating
degree-days*
(oC-days/year)
SASO climatic
zone
Weather
condition
Middle
Riyadh Riyadh 5,688 291 Zone-1 Riyadh
Hail Hail 4,428 601 Zone-2 Tabuk
Qassim Burydah 5,361 389 Zone-2 Riyadh
East
Eastern Region Dhahran 5,953 142 Zone-1 Dhahran
Al-Jouf Al-Jouf 4,128 859 Zone-2 Tabuk
Northern Borders Turaif 3,395 1,168 Zone-3 Tabuk
West
Tabuk Tabuk 5,359 571 Zone-2 Tabuk
Makkah Makkah 7,549 0Zone-1 Jeddah
Madinah Madinah 6,680 9Zone-1 Jeddah
South
Asir Abha 3,132 486 Zone-3 Abha
Jazan Jazan 7, 3 47 0Zone-1 Jazan
Najran Najran 5,605 12 Zone-1 Jazan
Al-Bahah 5,543 11 Zone-2 Abha
This study developed energy models for the
54 different housing prototypes (all possible
combinations of building type, vintage, and
location, from the three types, three vintages,
and six locations dened) to represent the energy
performance of Saudi Arabia’s existing residential
building stock. We generate the energy consumption
values, ECc,v,l for the building energy models through
detailed simulation analysis. Thus, the following
equation represents the overall energy consumption,
ECS, of residential building stock, made up of NS
housing units:
!=∑
",$,%",$,% . !.",$,%
(1)
Where fc,v,l corresponds to the fraction of housing
units that belongs to each combination of building
type, vintage, and location. As an alternative to Eq.
(1), the energy consumption of the building stock
can be predicted using the oor area related to
each set of characteristics, Ac,v,l, and the energy use
intensity (EUI), EUIc,v,l, values that can be derived for
the detailed simulation analysis of the 54 building
energy models:
!=∑",$,%",$,% .",$,%
(2)
GaStat (2018) reports the vintage distribution (i.e.,
fc,v,l) for the entire housing stock for each province,
13
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Description of the Residential Building Stock Model
and the data needed to estimate oor areas by
building types and provinces (i.e., Ac,v,l). Thus, the
energy consumption associated with the residential
building stock can be estimated as follows:
!=∑
",$,%. ",$,%",$,% .",$,%
(3)
The bottom-up stock model represented by Eq. (3)
can predict energy consumption for hourly, monthly,
and annual timescales. The following section
presents the calibration methodology to estimate
the contribution of each region to total national
electricity consumption.
REEM Calibration
As described in the previous section, regional
energy consumption is estimated by applying the
EUI of each prototype to the corresponding total
livable oor area for each building type and vintage
in each province (as depicted in Figure 1). The total
energy consumption of each province is aggregated
into the four Saudi regions (central, east, west, and
south). This allows us to calibrate REEM against
the actual regional consumption reported annually
by ECRA. Due to the lack of publicly available data
for certain building stock characteristics, such as
lighting intensity, the energy efciency of equipment,
and usage patterns, and the inability to directly
estimate them, the model’s parameters can be
adjusted to achieve a reasonable match with actual
consumption.
As an example of the calibration ow, we simulated
the residential building stock for 2018 and calibrated
the results against actual electricity consumption by
sector reported by SAMA (2018). Figure 3 illustrates
the predictions of the building stock model for
total energy consumption in the four regions and
the entire country after a systematic calibration
procedure. Specically, three main parameters have
been adjusted for the energy models specic to the
building types and vintages:
Figure 3. Comparison between REEM predictions and reported electricity consumption by region (2018).
Source: SAMA (2018).
0
20
40
60
80
100
120
140
Middle East West South Total
Annual Electricity Consumption
(TWh/year)
Actual Regional Consumption REEM Regional Consumption
14
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Description of the Residential Building Stock Model
(i) AC system energy efciency ratio (EER) for new
housing vintages was adjusted to 9.0 to reect
minimum SASO efciency requirements (2018a);
(ii) fan pressure drop for the AC was lowered
(because most systems are ductless);
(iii) lighting power density was decreased to reect
the more prevalent use of compact uorescent lamp
(CFL) and light emitting diode (LED) lighting xtures
in recent years, even in old buildings.
After the calibration procedure, we achieved a good
agreement between predictions from the building
energy stock model and reported SAMA data, with
relative errors of less than 2% for all regions and the
entire country.
The model provides a distribution of the total
energy consumption for residential building stock
by housing type, vintage, and location. This can
help policymakers determine the potential of each
category and design effective energy efciency
programs. For example, REEM indicates that
in 2018, single-family villas and apartments
consumed the most electricity among housing
types, representing 32% and 31% of total residential
electricity use, respectively, followed by 21% for
traditional houses and 16% for other categories
such as oor units in subdivided villas and traditional
houses. Villas and apartments represent 70% of
the livable oor area of the Kingdom’s residential
buildings. Therefore, tailored programs specic
to villas and apartments should be prioritized
to improve the energy efciency of the existing
residential building stock.
Figure 4. REEM estimated 2018 monthly electricity consumption by residential sector and region.
0
2
4
6
8
10
12
14
16
18
20
123456789101112
Monthly Electricity Consumption (TWh/month)
Middle East West South
15
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Description of the Residential Building Stock Model
REEM can also determine monthly electricity
consumption by residential buildings in Saudi
Arabia, categorized by housing type, vintage and
region. These results can deliver useful insights into
how household energy use responds to changes in
ambient temperature throughout the year, and hence
which energy standards and energy efciency
retrots would be most effective. For instance, in
2018, we observed high electricity demand during
the summer for the central and western regions,
both characterized by high population density and
hot climates, as shown in Figure 4. For such areas,
energy efciency measures that decrease space
cooling loads would be effective in reducing both
energy consumption and peak demand. The more
temperate and sparsely populated southern region,
on the other hand, did not signicantly contribute
to the nationwide increase in demand during the
summer. Furthermore, REEM provides the annual
end-use distribution of energy consumption,
disaggregated by building type, vintage, and
location, for space cooling, space heating, lighting,
equipment, hot water, and other functions.
16
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Evaluation of Energy Efciency
Measures
Following calibration, in its second phase,
REEM assesses energy efciency retrots
in terms of total electricity consumption and
peak demand reductions. The model considers
a wide range of EEMs that can improve various
building energy systems by retrotting the following:
building envelope components (adding
insulation with different R values1 to walls and
roofs, replacing single-pane windows with
double-paned ones, reducing air leakage,
adding window overhangs);
lighting systems (using high-efciency lighting);
appliances (replacing refrigerators, freezers,
washing machines, dryers and other appliances
with Energy-Star rated equipment);
air-conditioning systems (using more efcient
cooling systems with higher EER ratings or
using different air-conditioning technologies);
occupancy behavior changes dened by cooling
temperature settings (e.g., increasing the
cooling set-point by 1 degree Celsius (oC) or
2oC, especially for unoccupied rooms);
roofs (deploying ‘cool roof’ reective coating on
outer roof surfaces).
REEM can apply the above energy retrots
individually or combine two or more to estimate
the effectiveness of different packages of EEMs,
factoring in thermal interaction. Evaluating energy
efciency retrots individually can help policymakers
identify the highest priority upgrades, along
with their economic and environmental impacts.
Combining different EEMs can guide more complex
policy options. With EEM prices surveyed, REEM
can also calculate the rates of return and payback
periods from both household and government
perspectives (the latter will be especially relevant
for policymakers evaluating nancing or monetary
incentives).
1 Insulation R-value is expressed in RSI (m2.oC/W) and R (hr.ft2.oF/Btu).
17
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Application: The Impact of Different
Energy Efciency Measures
We utilized REEM to estimate the possible
electricity savings in 2018 from the
application of a set of energy efciency
measures with the below specications:
wall (R8) and roof (R18) thermal insulation
double-clear and low-emissivity window glazing
one- and two-meter window overhangs
50% and 70% reduction in air inltration
50% and 70% reduction in lighting electricity
consumption
30% and 65% reduction in equipment electricity
consumption
increasing the cooling setpoint by 1oC and 2oC
enhancing the AC unit’s EER to 10 and 12
coating the roof with highly reective material
(SR=0.6)
The results of the analysis, depicted in Figure 5,
conrm that retrotting AC systems has the greatest
potential for reducing the energy consumption of
Saudi Arabia’s residential building stock. Other
impactful retrot measures include the addition of
thermal insulation in walls and roofs. Conversely,
double-glazed windows are the least effective
building envelop upgrade, for two reasons. First,
some buildings already have them installed
(including all new homes, to comply with SASO
Figure 5. Predicted savings in annual electricity consumption for various energy efciency measures in 2018.
(a) Annual energy savings by region
0
5
10
15
20
25
30
35
R8 Wall Insulation
R18 Wall Insulation
R8 Roof Insulation
R18 Roof Insulation
Windows Double Clear Glazing
Windows Double Low-e Glazing
0.5 m Window Overhang
1 m Window Overhang
50% Infiltration Reduction
70% Infiltration Reduction
50% Lighting Reduction
70% Lighting Reduction
30% Equipment Reduction
65% Equipment Reduction
Increasing Cooling Set Point by 1 °C
Increasing Cooling Set Point by 2 °C
AC EER10
AC EER12
Cool Roof
Savings in Annual Electricity
Consumption (TWh/year)
Middle East West South
18
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
(b) Annual energy savings by housing type
thermal regulations); second, the window-to-wall
ratio for most residential buildings is relatively low
(typically about 15%). As expected, retrotting the
housing stock in the central and western regions
can achieve the greatest energy savings, since
the majority of the population and housing units
are located in these areas. Among housing types,
villas and apartments hold the greatest potential for
energy savings, as Figure 5(b) indicates.
As indicated in the previous analysis, replacing
existing AC systems with high-efciency units has
the most signicant impact in reducing energy
consumption for the Kingdom’s residential housing
stock. Table 4 summarizes the current Minimum
Energy Performance Standards (MEPS) for AC
systems in Saudi Arabia, including their required
EER values when the outdoor dry-bulb temperature
is 35oC (T1) and 46oC (T3) (SASO 2018a).
Table 4. SASO 2663 requirements for window and split ACs in Saudi Arabia.
Source: SASO (2018a).
Type of air conditioner January 1, 2018
Cooling capacity (CC) at T1
conditions in Btu/hr
EER at T1 EER at T3
Window AC CC ≤ 24,000 9.8 7.0
24,000 < CC ≤ 65,000 9.0 6.2
Split AC CC ≤ 65,000 11.8 8.30
T1: EER test conditions at 35°C outside, 27°C dry bulb inside and 46.6% relative humidity.
T3: EER test conditions at 46°C outside, 29°C dry bulb inside and 38.2% relative humidity.
Application: The Impact of Different Energy Efciency Measures
0
5
10
15
20
25
30
35
R8 Wall Insulation
R18 Wall Insulation
R8 Roof Insulation
R18 Roof Insulation
Windows Double Clear Glazing
Windows Double Low-e Glazing
0.5 m Window Overhang
1 m Window Overhang
50% Infiltration Reduction
70% Infiltration Reduction
50% Lighting Reduction
70% Lighting Reduction
30% Equipment Reduction
65% Equipment Reduction
Increasing Cooling Set Point by 1 °C
Increasing Cooling Set Point by 2 °C
AC EER10
AC EER12
Cool Roof
Traditional Houses Villas Floors in Traditional Houses Floors in Villas Apartments Others
19
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
(a) Annual AC efciency savings by region
Figure 6. REEM predictions of savings in annual electricity consumption for various AC efciency ratings by (a)
region and (b) housing type in 2018.
AC systems with higher EER, especially split-unit
types, are currently available in Saudi Arabia,
and would result in greater savings than those
meeting only minimum standards. The government
launched the High-Efciency AC (HEAC) Initiative
in early 2019 to encourage the local production
of highly energy-efcient cooling systems and
motivate citizens (i.e., only Saudi nationals qualify)
to purchase systems with EERs of at least 13 by
providing a 900 Saudi riyal (SAR) (240 United States
dollars [US$]) discount per unit. To examine the
impact of such a large-scale retrot program, we ran
REEM to simulate the impact of replacing all current
AC units with more efcient systems, ranging from
EER 9 to EER 16.5. The results show that upgrading
all existing AC units with EER 13 systems could
have reduced annual electricity consumption by
up to 35 terawatthours per year (TWh/year). If all
Saudi households installed EER 16.5 units, this
would increase the savings up to 47 TWh/year.
Figures 6(a) and 6(b) show the reductions in energy
consumption for the range of EER ratings by region
and building type, respectively.
As expected, replacing AC units in houses located
in the central and western regions achieves the
highest energy savings. For housing types, villas
and apartments offer the greatest impact. From
the individual household perspective, the payback
period of replacing an existing EER 8 AC unit with
an EER 13 one, factoring in the 900 SAR discount,
ranges from 4.6 to 5.9 years, depending on the
Application: The Impact of Different Energy Efciency Measures
0
5
10
15
20
25
30
35
40
45
50
EER 9 EER 9.8 EER 10.8 EER 11.8 EER 13 EER 14.5 EER 16.5
Middle East West South
20
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Application: The Impact of Different Energy Efciency Measures
operating conditions. Our analysis shows that the
government can quickly recover its spending on
consumer EEM incentives through the resulting
reduction in domestic fuel consumption, which frees
up oil to be exported at higher international market
prices. For example, if all current AC units had been
(b) Annual AC efciency savings by building type
replaced with EER 13 systems through the HEAC
program in 2018, the government would have spent
US$5.95 billion but increased its annual fuel exports
by US$3.0 billion, thereby regaining its investment in
less than two years (depending on oil prices).
0
5
10
15
20
25
30
35
40
45
50
EER 9 EER 9.8 EER 10.8 EER 11.8 EER 13 EER 14.5 EER 16.5
Traditional Houses Villas Floors in Traditional Houses Floors in Villas Apartments Others
21
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Summary and Future Work
REEM was developed utilizing an engineering
bottom-up approach. It incorporates
energy models for 54 building prototypes
representative of Saudi Arabia’s existing housing
stock. The housing stock is dened by the key
characteristics of building type, vintage, and
location, and is based on available governmental
data and other published studies and statistics.
REEM can evaluate energy consumption and end
uses for the Kingdom’s entire residential building
stock, and simulate electricity consumption and
peak demand, disaggregated by building type,
vintage, and location, for hourly, monthly, and
annual time scales. The regional total electricity
consumption generated by REEM can be validated
using actual regional residential electricity
consumption data.
REEM can also estimate the impact of various
retrot measures on electricity consumption, peak
demand, and carbon emissions. Our analysis
indicates that the effectiveness of an energy
efciency upgrade depends on building type,
vintage, and, most signicantly, the location of the
targeted stock. In addition, the model can simulate
the economic and environmental consequences of
different combinations of retrot measures. REEM
can therefore provide valuable insights for designing
energy efciency programs for the residential sector
in Saudi Arabia.
REEM was developed primarily to provide
policymakers with useful insights on how the
Kingdom’s residential building stock consumes
energy and the potential of different energy
efciency programs in reducing demand. Therefore,
subsequent studies can utilize REEM to model the
impact of energy efciency programs on residential
energy consumption patterns in Saudi Arabia,
including whether households exhibit a ‘rebound
effect,’ increasing their consumption in response to
the lower electricity bills achieved through energy
efciency measures. REEM can also be expanded
to estimate the impact of changes in electricity
prices on electricity demand in both the short and
long run.
22
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
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24
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Notes
25
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
Notes
26
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
About the Authors
Mohammad Aldubyan
Mohammad is a researcher in KAPSARC’s Climate and Environment
program. His research focuses on energy demand and energy efciency.
He is currently part of a project modeling energy demand in Saudi Arabia
and estimating the economic impacts of energy price reform. He is also
modeling Saudi Arabia’s residential building stock to assess energy
efciency retrot options. Mohammad holds an M.Sc. in renewable and
clean energy from the University of Dayton, Ohio.
Moncef Krarti
Moncef is a visiting researcher with over 30 years of experience designing,
testing, and assessing innovative energy efciency and renewable energy
technologies applied to buildings. He is a professor and coordinator
of the Building Systems Program, Civil, Environment and Architectural
Department at the University of Colorado.
Eric Williams
Eric has over 20 years of experience as an energy and environmental
economist, focusing on energy and climate change policy, energy
systems analysis, and climate change mitigation and adaptation options.
He recently managed the writing and publication of a series of reports
on Circular Carbon Economy, which were delivered to the G20 in 2020.
They were written by organizations including the International Energy
Agency (IEA), the OECD, the International Renewable Energy Agency
(IRENA), the Nuclear Energy Agency (NEA), and the Global CCS Institute
(GCCSI). Eric was previously acting program director for the Climate and
Environment program at KAPSARC. Before joining KAPSARC, he worked
as an economist with the North Carolina Utilities Commission and was a
consultant at the OECD. Eric has previously worked for the United Nations,
academic research institutes, think tanks, and government.
27
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
About the Project
In Saudi Arabia, the residential sector consumes most of the country’s electricity and is
the primary driver of peak electricity demand. The Residential Energy Model (REEM)
simulates the Kingdom’s entire building stock to provide insight to policymakers seeking
to understand the impact of housing stock on the electricity market and national economy,
and to develop and implement effective energy efciency policies.
REEM is part of the Modeling Residential Energy Demand and Energy Efciency
in Saudi Arabia project, which aims to accurately model the country’s entire residential
building stock. The project’s key goals are (i) to better understand the current status of
the Kingdom’s housing sector in terms of energy consumption, and (ii) to assess the
potential of different energy efciency programs and demand-response management to
reduce electricity demand from the perspective of both households and the government.
More broadly, the project aims to help KAPSARC conduct technical, economic, and
environmental assessments of residential demand-side management options, and in turn
to support policymakers seeking to design impactful energy strategies for Saudi Arabia’s
housing sector.
28
Residential Energy Model for Evaluating Energy Demand and Energy Efciency Programs
in Saudi Residential Buildings
www.kapsarc.org