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Engineering, Technology & Applied Science Research Vol. 10, No. 1, 2020, 5191-5194 5191 
 

www.etasr.com Zerroug & Dzelzitis: A Study of Modeling Techniques of Building Energy Consumption 

 

A Study of Modeling Techniques of Building Energy 

Consumption 
 

Abdellah Zerroug 

Electrical Engineering Department 

Ferhat Abbas Setif-1 University 
Setif, Algeria 

abzerroug@gmail.com 

Egils Dzelzitis 

Institute of Heat, Gas and Water Technology 

Riga Technical University 
Riga, Latvia 

egils@lafipa.lv 
 

 

Abstract—Residential energy consumption accounts for more 

than 40% of the total energy consumed in the world. The 

residential sector is the biggest consumer of energy in every 

country, and therefore focusing on the reduction of energy 

consumption in this sector is very important. The energy 
consumption characteristics of the residential sector are very 

complicated and the variables affecting the consumption are wide 

and interconnected, so more detailed models are needed to assess 

the impact of adopting efficient and renewable energy 

technologies suitable for residential applications. The aim of this 

paper is to review some of the techniques used to model 

residential energy consumption. They are gathered in two 

categories: top-down and bottom-up. The top-down approach 

considers the residential sector as an energy sink and does not 

take into account the individual end-uses. The bottom-up 

approach uses the estimated energy consumption of a 

representative set of individual houses and extrapolates it to 

regional and national levels. Based on the strengths, 

shortcomings, and purposes, an analytical review of each 
technique, is provided along with a review of models reported in 

the literature.  

Keywords-energy consumption; modeling techniques; 

residential sector; top-down; bottom-up; CO2 emissions 

I. INTRODUCTION  

Residential Building Energy Consumption (RBEC) is 
defined as the energy consumed by households excluding 
transportation uses. In the residential sector, energy is used for 
equipment and to provide heating, cooling, and lighting. It is 
affected by income, energy prices, building location, household 
characteristics, weather, type and efficiency of equipment, 
energy access, availability, and energy related policies. As a 
result, the type and amount of energy can vary widely within 
and across regions and countries [1]. In recent years, although 
measurements and policies have been taken to reduce energy 
consumption and gas emissions, and the residential buildings 
have continuously improved in efficiency, substantial 
differences in the residential energy consumption are still being 
observed in similar dwellings [2-4]. Globally, residential 
energy consumption represents the 16-50% of the total and 
averages approximately at 30% as shown in Figure 1 [5]. Only 
10% of the population of the world exploits 90% of fossil fuel 
resources. Today's energy systems rely heavily on fossil fuel 

resources making them diminish ever faster. The world must 
prepare for a future without fossil fuels. Sustainable energy 
consumption has become an urgent matter for all countries. 
World residential delivered energy consumption will increase 
by 57% from 2010 to 2040 (Table I) [1]. 

 

 
Fig. 1.  Residential energy consumption shown as a percentage of national 

energy consumption and in relative international form [1] 

In Europe the built environment consumes 40% of the 
produced energy. A large part of this energy is consumed in 
residential buildings. Households account for about 30% of the 
total building-related energy consumption in OECD countries 
[6]. A 30-57% of the energy consumed by households is spent 
on space and domestic water heating, making conservation in 
this area a matter of vital importance [2]. This significant 
consumption level indicates the crucial role that the residential 
sector plays in total energy consumption, which means that it is 
necessary to understand the characteristics associated to energy 
consumption to better prepare for the increasing energy 
demand in the future. In response to climate change, high 
energy prices, and energy supply/demand, there is an interest in 
understanding the detailed consumption characteristics of the 
residential sector in an effort to promote conservation, 
efficiency, technology implementation, and energy source 
switching, such as to on-site renewable energy[1]. 

Other sectors such as commercial, agriculture, transport and 
industry have a regular energy consumption because usually 
these sectors are private or under centralized ownership and 
they are well defined and regulated. The energy consumption 
in the residential sector is very complex because of the large 
variety of construction types, sizes, thermal envelope materials, 
and the very wide variety of occupant behavior. 

Corresponding author: Abdellah Zerroug



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www.etasr.com Zerroug & Dzelzitis: A Study of Modeling Techniques of Building Energy Consumption 

 

TABLE I.  GLOBAL RESIDENTIAL DELIVERED ENERGY CONSUMPTION 

Region 2010 2015 2020 2025 2030 2035 2040 Average annual percent change 2010-2040 

OECD 28.2 28.1 29 29.9 30.8 31.3 32 0.4 

Americas 13.2 12.8 12.9 13.2 13.5 13.9 14.2 0.3 

Europe 11.7 11.9 12.5 13.1 13.5 13.7 13.9 0.6 

Asia 3.3 3.4 3.5 3.7 3.8 3.8 3.9 0.5 

Non OECD 23.9 27 30.8 35.1 40.0 45.0 49.8 2.5 

Europe and Eurasia 6.3 6.3 6.7 7.1 7.7 8.1 8.6 1.0 

Asia 10.6 12.8 15.6 18.7 22.2 25.9 29.6 3.5 

Middle East 3.4 3.9 4.2 4.4 4.6 4.7 4.8 1.2 

Africa 1.6 1.7 1.9 2.2 2.5 2.8 3.2 2.4 

Central and S. America 2.0 2.3 2.4 2.7 3.0 3.4 3.7 2.1 

World 52.0 55.1 59.8 65.0 70.8 76.3 81.8 1.5 

 

Space heating and cooling, domestic hot water, appliances, 
and lighting are the major utilities that consume most energy in 
residential buildings. Total Residential Energy Consumption 
(REC) is defined as the energy required by the aggregates, 
including the losses due to energy transmission or appliances 
efficiencies. The energy consumed for heating and cooling 
space has the biggest share of the total REC. This energy can 
be supplied by different sources including passive solar gain, 
occupants gain, lighting and appliances gain. The total REC for 
a given area or a country gives the regional or national 
residential sector consumption.  

The mean object of the building energy consumption 
modeling is to quantify the energy needs and requirements as a 
function of input parameters. The most common reason for 
using models is to determine the energy supply requirements at 
a large scale, and the change in energy consumption in a 
dwelling due to renovation, retrofit, or improvement of 
equipment at a local scale. Modeling REC can be very useful 
for policy makers and householders. REC models depend on 
climate variations, thermal zone, building construction, 
neighborhoods, local population, life standards, region or 
nation. Our objective is to provide an up-to-date review of 
different modeling techniques used for modeling REC. Two 
main approaches are identified: top-down and bottom-up. Each 
method depends on different levels of input data information, 
different calculation or simulation techniques, and shows 
results with different applicability. A detailed review of each 
technique, focusing on the strengths, shortcomings, and 
purposes, is provided along with a review of the reported 
models. 

II. OVERVIEW OF MODELING METHODOLOGIES 

In this section, we intend to outline the methodologies and 
the available techniques for modeling residential sector, as this 
has already been given in detail elsewhere [5, 7–11]. 
Residential energy models strongly depend on input data to 
calculate or simulate energy consumption. The level of detail of 
the available input data can vary dramatically [11], resulting in 
the use of different modeling techniques which seek to take 
advantage of the available information. Modeling and control 
of the air temperature and humidity in greenhouses in order to 
reduce energy consumption and keep a suitable microclimate 
are discussed in [12]. Measures to control photovoltaic energy 
use and shading effect on energy consumption are developed in 
[13]. The measurements taken to reduce energy consumption in 
residential building should not affect thermal comfort 

conditions. Some authors have suggested and studied the effect 
of facade shading and green roofs in conserving thermal 
comfort with less consumed energy [14-16]. These different 
modeling techniques have different strengths, weaknesses, 
capabilities, and applicabilities. The input data necessary to 
build residential energy models include information on the 
physical characteristics, occupants and their appliances, 
historical data of energy consumption, climatic conditions, and 
macroeconomic indicators of the dwellings, depending on the 
modeling methodology to be used. The preliminary estimate of 
the total residential sector energy consumption is usually 
published by governments which compile gross energy values 
submitted by energy providers (Canada [11], USA [17], UK 
[18], and China [19]). These energy estimations give a good 
indication for energy consumption but may not be accurate as 
they do not take in account the onsite energy gain or 
generation. A more detailed source of energy consumption 
data, typically on a monthly basis for each dwelling, is the 
billing records of energy suppliers. However, with no 
additional housing information these energy consumption 
values are difficult to correlate due to the wide variety of 
dwellings and occupants.  

Housing surveys are conducted to provide more detailed 
information about equipment energy consumption values. The 
target of these surveys is a sample of residential dwellings to 
determine building properties and occupant characteristics and 
appliances penetration levels (Canada [20], USA [21], and UK 
[22]). Usually, surveys aim to define the physical properties of 
the house such as geometry and thermal properties of the 
envelope, ownership of appliances, occupants and their use of 
appliances and preferred settings, and demographic 
characteristics. In addition, surveys may attempt to obtain the 
energy suppliers’ billing data and alternative energy source 
information (e.g. unreported wood usage) to correlate the 
energy consumption of the house with its characteristics 
identified during the survey. This will permit the calibration 
through reconciliation of a model’s predicted energy 
consumption with actual energy billing data. This level of 
information is superior to the previously mentioned energy 
supplier values, however it is limited due to collection 
difficulties and cost, and therefore it is imperative that the 
selected sample be highly representative of the population. 
Also, occupant descriptions of their appliance use are highly 
subjective and can be influenced by the season during which 
the survey takes place [20]. Some examples of surveys which 
have been condensed for the purpose of energy simulation are 



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[21-23]. Estimated total sector energy, individual billing data, 
surveys, and sub-metering have been used to varying degrees 
in the development of residential energy consumption models. 
Deciding which information is used depends on availability 
and model’s purpose. The purpose of models ranges widely 
and may be directed towards determining supply requirements, 
price and income elasticity, the energy consumption impacts of 
upgrades, or changes to behavioral patterns.  

III. RESIDENTIAL ENERGY USE MODELING METHODOLOGIES 

The complexity of residential energy use patterns and the 
dependence on data input level make modeling residential 
energy use potentially challenging. However, based on 
different capabilities, strengths, weaknesses, and the 
applicability of each modeling technique, matching input data 
with models that can best use them can produce satisfactory 
models. Generally speaking, techniques employed to model 
residential energy use can be classified into two categories, 
“top-down” and “bottom-up”, and this terminology is referred 
to the hierarchal position of data inputs [5]. Top-down models 
are mostly uses at a macroscopic level, by governments 
building departments while bottom-up models are used at 
microscopic level by engineers (see [5] for details).  

A. Top-down Models 

The use and development of the top-down modeling 
approach proliferated with the energy crisis of the late 1970s. 
In an effort to understand consumer behavior with changing 
supply and pricing, broad econometric models were developed 
for national energy planning. These models require little detail 
of the actual consumption processes. The models treat the 
residential sector as an energy sink and regress or apply factors 
that affect consumption to determine trends. Most top-down 
models rely on similar statistical data and economic theory. As 
the housing stock in most regions is continuously undergoing 
improvement and increase, simply modeling the energy 
consumption solely as a function of economic variables is short 
termed. Authors in [24] initiated an annual housing energy 
model of the USA. Their model relied on econometric 
variables and included a component for growth/contraction of 
the housing stock. Their work was expanded and improved 
over the following years resulting in an econometric model 
which had both housing and technology components [25, 26]. 
The housing component evaluates the number of houses based 
on census data, housing attrition and new construction. The 
technology component increases or decreases the energy 
intensiveness of the appliances as a function of capital cost. 
The economic component evaluates changes in consumption 
based on expected behavioral changes and efficiency upgrades 
made to the technology component. Finally, market penetration 
is considered a function of income and demand/supply. The 
simulation model combines the changes in outputs of the 
components and estimates the energy consumption given 
historic energy consumption values. The authors felt their 
model was sensitive to major demographic, economic and 
technological factors, but recognized the need to continually 
update all assumed information to improve quality. Authors in 
[25] developed a similar model for New Zealand although it 
had a technological focus.  

B. Bottom-up Models 

The bottom-up approach was developed to identify the 
contribution of each end-user towards the aggregate energy 
consumption value of the residential stock. This refines the 
understanding of the details associated with the energy 
consumption. There are two distinct categories used in the 
bottom-up approach to evaluate the energy consumption of 
particular end-uses. The statistical models (SMs) utilize 
dwelling energy consumption values from a sample of houses 
and a variety of techniques to regress the relationships between 
the end-uses and the energy consumption. SM models can 
utilize macroeconomics, energy prices, income, and other 
regional or national indicators, thereby gaining the strengths of 
the top-down approach. The engineering models (EMs) 
category relies on information of the dwelling characteristics 
and end-uses themselves to calculate the energy consumption 
based on power ratings and use characteristics and/or heat 
transfer and thermodynamic principles. Consequently, the 
engineering technique has strengths such as the ability to model 
new technologies based solely on their traits. Once developed, 
the bottom-up models may be used to estimate the energy 
consumption of houses representative of the residential stock 
and then these results can be extrapolated to be representative 
of the regional or national residential sector.  

IV. CONCLUSION 

Top-down models are used mostly for supplying analysis 
based on long-term projections of energy demand by taking in 
account the historical response. Bottom-up statistical models 
are used to identify the energy demand contribution of end-uses 
by introducing the behavioral aspects based on the data 
obtained from energy authorities and surveys. Bottom-up 
engineering techniques are used to explicitly calculate energy 
consumption of end-uses taking in account the detailed 
descriptions of a representative set of houses, and they have the 
capability of determining the impact of new technologies. 
Given today’s energy considerations that include supply, 
efficient use, and effects of energy consumption to the 
promotion of conservation, efficiency, and technology 
implementation, all modeling approaches are useful. Top-down 
models are more useful in supply considerations because they 
are strongly weighted by historical energy consumption which 
makes their estimates of supply more accurate. Bottom-up 
statistical models can account for occupant behavior and use of 
major aggregates, leading to understand which of behaviors 
and end-uses cause more energy consumption. Lastly, bottom-
up engineering models may identify the impact of new 
technologies based on their characteristics and account for the 
wide degree of variety within the housing stock. To determine 
the impacts of such new developments requires a bottom-up 
model with more focus on efficiency and surveys on energy 
consumption generation at individual houses. In this fast 
technological development and implementation environment, 
the bottom-up techniques will likely provide much utility as 
policy and strategy development tools. 

Although bottom-up building physics stock models are 
used to explicitly determine and quantify the impact of 
different combinations of technological measures on delivered 
energy use and CO2 emissions, and therefore represent an 



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important tool for policymakers, there are some limitations 
associated with modeling. The most important shortcoming of 
all these models is their lack of transparency and quantification 
of inherent uncertainties. The lack of publicly available 
detailed data on the models inputs and outputs, as well as 
underlying algorithms renders any attempt to reproduce their 
outcomes problematic. In addition, the relative importance of 
input parameter variations on the predicted demand outputs 
needs to be quantified.  

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