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Analysis of energy demand in residential buildingsfor different climates by means of dynamicsimulationVincenzo Biancoa, Mattia De Rosaa, Federico Scarpaa & Luca A. Tagliaficoa

a University of Genoa – DIME/TEC - Division of Thermal Energy and EnvironmentalConditioning, Via All'Opera Pia 15 A –16145 Genoa – ItalyAccepted author version posted online: 25 Mar 2014.Published online: 25 Mar 2014.

To cite this article: Vincenzo Bianco, Mattia De Rosa, Federico Scarpa & Luca A. Tagliafico (2014): Analysis of energydemand in residential buildings for different climates by means of dynamic simulation, International Journal of AmbientEnergy, DOI: 10.1080/01430750.2014.907207

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1

Analysis of energy demand in residential buildings for different climates by means of dynamic

simulation

Vincenzo Bianco*, Mattia De Rosa, Federico Scarpa, Luca A. Tagliafico

University of Genoa – DIME/TEC - Division of Thermal Energy and Environmental Conditioning

Via All'Opera Pia 15 A –16145 Genoa – Italy

*Corresponding author: vincenzo.bianco@unige.it; Phone: +39 010 353 2872

Abstract

The aim of the present paper is to propose an analysis of energy consumption of a standard building in different

climates. The analysis is developed by simulating the dynamic behavior of the building subjected to different climatic

conditions according to the considered location. Simulations are performed by means of an in-house developed code,

validated by comparison with the outcomes from leading software, particularly TRNSYS and EnergyPlus. The use of a

self-developed code guarantees a high flexibility and allows the implementation of new capabilities if necessary.

The impact on the energy consumption of various parameters, namely internal and external wall insulation, window

surface areas, thermal capacity and orientation, is investigated. Results show that the insulation of external walls has a

fundamental role in reducing energy consumption, because it allows to exploit the thermal capacity of the walls. This is

particularly useful for buildings which necessitate to keep the internal temperature constant.

Keywords: building dynamic simulation; space heating; energy consumption; energy efficiency; energy modeling.

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Nomenclature

Symbols

α solar height angle (deg)

C thermal capacitance, J K-1

EUI energy use intensity

HDD heating degree days, °C-day

Ib,n Normal direct solar radiation (W m-2)

Id,h Horizontal diffuse solar radiation (W m-2)

Id,n Diffuse solar radiation on surface (W m-2)

Ij,n Global solar radiation on surface j (W m-2)

q heat transfer rate, W

ξ tilt solar radiation coefficient

MDDH Mean daily degree-hours, °C-day

R thermal resistance, m2-K W

-1

T temperature, K

t time, s

V volume, m3

Subscripts

b base case

cs cooling system

d day

e external

f floor

fg free gain

gw window

h hour

hs heating system

i internal

j node index

r roof

sg solar gain

v ventilation

w wall

1.0 Introduction

Buildings are responsible for a large share of energy consumption and CO2 emissions, therefore it is fundamental to

enhance their energy performance. Energy efficiency in buildings is collecting a strong interest in developed and

developing countries. Developed countries try to implement policy measures in order to reduce the energy use intensity

(EUI) of buildings by promoting the retrofitting of existing buildings, for example by supporting the enhancement of

the walls insulation, the installation of efficient windows or the substitution of old boilers with more efficient ones. The

potential for energy savings is recognized to be very high, as clearly highlighted in Cengel (2011).

On the other hand, in developing countries, such as China, the building energy consumption represents about 40% of

the global energy demand Yang et al. (2013). Limiting the energy utilization in buildings by introducing energy

efficiency policies enables the avoidance of a sharp increase of primary energy consumption. This would eliminate the

problem of guaranteeing the supply of large quantities of energy, mostly fossil fuels, for long periods. In fact, as

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reported in Yang et al. (2013) and Zhou et al. (2013), a debate is growing on the topic and different strategies are

considered to implement successful energy saving measures.

To achieve significant energy savings in buildings, it is mandatory that they are correctly designed, and therefore, the

utilization of effective decision making tools is mandatory. These tools allow estimating the future energy demand and

the parameters which affect the consumption, in order to minimize the energy intensity. To this purpose, many

researchers proposed different investigations aiming at supporting the energy efficient design of different kind of

buildings.

Guechchati et al. (2012) proposed a dynamic analysis of an individual house in Morocco. They analyzed the impact of

different parameters, such as insulation, thermal mass and windows, on the energy consumption, highlighting a large

potential for energy saving.

Catalina et al. (2008) developed regression models to predict the monthly heating demand for single-family residences

in temperate climates. The database to perform the regressions was obtained by executing dynamic simulations, by

means of a commercial available tool, on different kind of buildings with various features. The very interesting aspect

of developing such kind of regressions is the possibility to develop very quick parametric studies in order to optimize

the building structure versus environmental or economic criteria.

Similarly, Mavromatidis et al. (2013) proposed a simple method based on classic and fractional factorial simulation

plans to obtain regression models in the form of polynomial functions that link the angle, the thermal conductivity and

the thickness of each envelope's component to the overall wall's thermal resistance. They built different simulation

scenarios according to basic fractional factorial simulation plans in order to obtain valid empirical polynomial

functions. The regression models' results show that the error caused by simplification is acceptable in most conditions,

and a lot of calculations could be saved.

The reduction of the complex numerical model to simple regression model in the form of polynomial equations aims to

assist architects and engineers to evaluate energy performances of buildings also in the early stages of the design

process.

Likewise, Attia et al. (2012) presented an energy-oriented software tool which provides informative support aiming at

facilitating decision making about zero energy buildings. A residential benchmark was established coupling sensitivity

analysis modeling and energy simulation software (EnergyPlus) as a means of developing a decision support tool to

allow designers to rapidly and flexibly assess the thermal comfort and energy performance of early design alternatives.

Malatji et al. (2013), instead, formulated a multiple objective optimization model to help decision makers to make an

optimal decision when investing in energy-efficient building retrofitting, with the aim to maximize the energy savings

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and to minimize the payback period for a given fixed initial investment. The model is formulated as a multi-objective

optimization problem with the net present value (NPV), initial investment, energy target and payback period as

constraints and it is solved using genetic algorithms (GAs). They also performed sensitivity analysis by analyzing the

influence of the auditing error of the facilities, wrongly specified energy savings, the initial investment, changes in

interest rate and the changes of electricity prices on the payback period, the maximum energy saved and NPV of the

investment. The outcome of this analysis proved the reliability of the proposed model.

Other authors tried to improve the effectiveness and speed of CFD simulations, in order to efficiently support designers.

To this end, Zhang et al. (2013) analyzed the combination of a computational fluid dynamics (CFD) simulation directly

with a network model. Usually, this approach is too computationally time-consuming, but they were able to develop an

acceptably fast simulation method that couples the contribution ratio of indoor climate (CRI), which is extracted from

CFD results and indicates the individual impact of all heat factors, with the network model to implement an energy

simulation that incorporates a temperature distribution. With the introduction of CRI, it is possible to achieve a

precision as high as that of CFD and a calculation speed as fast as that of the network model.

Ham and Golparvar-Fard (2013), instead, presented a methodology to compare images from thermal cameras and

results of CFD simulations, in order to analyze the differences between simulated and measured data. Their results

demonstrated that the proposed methodology facilitates calibration of building energy performance models and supports

detection and analysis of building performance deviations.

Other studies are focused on the identification of relevant, but simple drivers which determine energy consumption. For

example, Granadeiro et al. (2013) proposed the utilization of a new parameter, Envelope-Related Energy Demand

(ERED), calculated starting from several building (surfaces and transmittances of the envelope elements, solar heat

gain, etc.) and site related characteristics (temperature, solar irradiation), which aims to overcome the shortcomings of

the shape factor while maintaining a reasonable simplicity of use. Their results highlighted that there is a strong

correlation between ERED and simulated energy demand. These results suggest the usage of ERED to assist design

decisions in early stages of the design process. Littlefair et al. (2010) analyzed the impact of solar shading on the air

conditioning of office buildings, concluding that the benefits of shading are latitude dependent.

Different authors investigated the performance of buildings energy simulation tools, in order to assess their

performances and differences, as is the case of Schwartz and Raslan (2013), who analyzed the performances of three

available building energy simulation tools and compared the results. Their analysis showed that different simulation

tools resulted in different energy consumption figures, but they had a minor effect on energy performance credit scores

currently in place in UK.

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Various investigations also considered the impact of HVAC or lighting systems on the energy consumption of

buildings. To this end, Goyal et al. (2013) proposed several algorithms to control the indoor climate of commercial

buildings and compared their performance and complexity by means of simulations. The goal of those control

algorithms was to use occupancy information to reduce energy use-over conventional control algorithms-while

maintaining thermal comfort and indoor air quality.

The aim of the present paper is to propose a parametric investigation of some main drivers, namely internal and external

wall insulation, thermal capacity, window surface and orientation, influencing energy consumption in buildings, in

order to understand their impact on the energy use intensity. Various climatic conditions for various countries are

considered, in order to highlight the different impact of the energy consumption drivers. The analysis is developed in

dynamic conditions in order to evaluate the impact of the thermal inertia of the building.

All the simulations are performed by using Building Energy Performance Simulator (BEPS), a tool specifically

developed at University of Genoa, able to simulate the energy flows related to the heating and cooling of various

buildings in different climatic conditions, considering a static or dynamic behavior.

2.0 Methodology

2.1 Building Energy Performance Simulator (BEPS)

BEPS is a flexible and versatile simulator developed at University of Genoa able to determine the heating and cooling

loads of a building, in order to guarantee the thermal comfort of the occupants. It can be used to support the design of

HVAC plants, to perform energy diagnosis of buildings, to estimate energy consumption and so on. At the same time,

its flexible structure easily allows the implementation of new capabilities, such as new models of HVAC plants, in order

to study the dynamic energy interactions between the building and its energy plants. BEPS is developed by using the

well known Matlab®

-Simulink® programming language.

The simulator is based on the lumped capacitance approach Crabb et al. (1987) and Nielsen (2005) and a unique

heating zone is taken into account considering an effective thermal capacitance in which all internal capacities are

lumped together Nielsen (2005).

As suggested in Boyer et al. (1996), transient energy balance equations have been written for each of the following

domains:

External walls

Roof

Floor

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Internal air and environment

For each of the above mentioned components, a proper lumped thermal capacitance C is taken into account, in order to

consider the overall thermal inertia. Therefore, the entire building is described by a system of first order ordinary

differential equations, which is solved using standard numerical techniques Dormand and Prince (1980), using a

variable time-step between 300 and 600s. A schema of this physical model is reported in Figure 1, while the block

diagram in Figure 2 describes the calculation procedure adopted in the present work.

The transient energy balance equations for the four components taken into account can be written as follows:

jsgjewjiw

jw

jw qqqdt

dTC ,,,

,

, External walls (1)

rsgerirr

r qqqdt

dTC ,

Roof (2)

fsgefif

f

f qqqdt

dTC ,

Floor (3)

gwwvfgcshsi

i qqqqqdt

dTC / Internal Air (4)

The heated zones of the building are modeled as a unique isothermal heated air volume with a global thermal

capacitance Ci (accounting for furniture, internal wall). The volume of internal air exchanges heat with the internal layer

of the walls and with the external air across the windows, while it is heated by the heating system and by the internal

free gain due to persons and equipment. The heat losses due to ventilation and the solar gain, arising from the global

solar radiation transmitted across the windows, are also taken into account, as shown in Figure 1(b). In particular, the

global solar radiation Ij,n for each wall j is calculated taking into account both direct normal radiation and horizontal

diffuse radiation, as shown in Eq. 5:

wrhdnbndjnbnj IsenIIII ,,,,,, cos (5)

where:

,b nI = normal direct solar radiation [W m-2];

hdI , = horizontal diffuse solar radiation [W m-2];

ndI , is the sky diffuse solar radiation on the surface which can be calculated according to Perez et al. (1990). j

represents the angle of incidence between solar radiation and surface normal axis, while α is the solar height angle.

Finally the term wr , in Eq. 5 is the tilt solar redirected radiation factor which depends on the external ground

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reflectivity. The radiation coming from the windows is supposed to be absorbed by the floor according with its

absorbance ( fsgq , in Eq. (3)); whereas the reflected part is assumed to be uniformly distributed on all the interior

surfaces ( jsgq , and rsgq ,

in Eq. 1,2), UNI/TS 11300-1 (2008).

External walls, floor and roof are modeled in the same way (Eq. 1-3), therefore they are characterized by the same

equation, but with different parameters, in particular:

- In Eq. (1), jwC , and

jwT , represent the thermal capacitance and the node temperature of the wall (Fig. 1(c)),

whereas jiwq , and jewq ,

represent the heat flux between the wall node and the internal/external wall surface

(Fig. 1(c)). Similar contributions are also considered in Eq. (2).

- sgq is considered in Eq. (1-3) in order to take into account the effect of the solar radiation transmitted across the

windows, Fig. 1(d).

Each wall is modeled considering two different layers: the internal one, which exchanges heat with the internal air

across the inner surface of the walls, and the external one, which is subjected to the combined effect of external air

convection and solar irradiation. One single node capacitance point, which takes into account the entire wall, is

normally located in the middle of the wall, but it is possible to move it depending on the characteristics of the wall

structure.

Regarding the internal air (Eq. 4), the following contributions are taken into account in the energy balance equation:

- cshsq / is the heating/cooling input thermal power, which depends on the system configuration and on the

regulation criteria adopted;

- fgq represents the internal free gain due to persons and equipment. In the present analysis a global averaged value

of free gain is considered and it depends on the useful surface of the building and its intended use UNI/TS 11300-1

(2008);

- vq refers to the heat transfer due to ventilation considering the minimum suitable value of air exchange UNI EN

15251 (2008);

- wq takes into account the heat transfer due to the transmission through the external walls, roof and floor. It is

calculated by summing the contribution of each wall, which depends on the specific thermo-physical characteristics

of the wall and on the operating environmental conditions;

- gwq considers heat transfer due to radiative transmission across the windows.

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2.2 Building description

A proper description of the building is of fundamental importance to assess its energy consumption, because of the

numerous parameters influencing the energy performance.

In order to perform a general analysis, a standard building block of two levels is considered, Fig. 1(a) . It is represented

by a parallelepiped with a squared floor of side equal to 10 m and height of 6 m and modeled with an unique heated

zone. The required internal temperature is 18.3 °C with a dead-band setting of ± 1 °C. All the relevant data concerning

the considered building are reported in Table 1, while the main thermo-physical data of each element is reported in

Table 2.

A specific orientation is also considered, because it largely influences the heating needs in winter and the cooling

requirements in summer. A detailed study of the orientation in the design phase of new buildings represents an effective

way to reduce energy consumption.

It is important to highlight that the considered building already has an insulation substrate applied on the walls: in fact

an insulation layer of thickness equal to 7 cm is applied on the external side of the wall, introducing a thermal resistance

of 1.771 m2K/W.

2.3 Climatic data

To perform a detailed energy analysis of a building, numerous climatic data are necessary in order to assess the heating

and cooling needs, which vary according to the geographical location.

In the present paper, latitude and altitude of specific locations are taken into account, because latitude is utilized to

calculate the solar radiation, whereas altitude is necessary to estimate the diffused incident radiation. These two

parameters allow estimating the thermal load due to solar radiation.

Then the hourly profiles of different climatic parameters are needed, in particular:

- external temperature;

- normal direct radiation and diffused horizontal radiation, in order to determine the value of the total incident

radiation on the surface;

- wind intensity and direction, utilized to determine the external convection coefficients for each surface.

All the climatic data considered in the present paper are taken from U.S. DOE (2013).

2.4 Model validation

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In order to demonstrate the validity and robustness of the results furnished by BEPS, a comparison with consolidated

simulators is proposed. Particularly, BEPS results are compared with simulation performed by means of TRNSYS and

EnergyPlus, which are considered to be the reference simulators in the field of the simulation of buildings energy

consumption.

The comparison is made in terms of heating energy consumption, cooling energy demand and incident radiation as a

function of the orientation. Calculations in TRNSYS and EnergyPlus are taken from Caputo et al. (2011), where an

analysis of a building with the same parameters of the one considered in the present paper is shown. Thanks to the

possibility to read directly the climatic file (U.S. Department of Energy, 2013), in TRNSYS and Energy Plus, no

difference occurs in inputs data between the codes.

Figure 3 reports the estimation of heating and cooling energy demand of our reference building for three Italian cities

with different climates, namely cold in Milan, moderate in Rome and warm in Palermo.

The comparison shows that the three simulators report very similar results in terms of heating demand, whereas in terms

of cooling demand Energy Plus is characterized by higher deviations with respect to TRNSYS and BEPS.

Figure 4 reports a comparison in terms of calculation of the incident radiation and it shows that the three simulators

guarantee very similar performances. The importance of this comparison is due to the simpler approach performed by

BEPS in calculating the total incident radiation.

The reported comparison shows that BEPS can be seen as a reliable simulator and its performances and results are in

line with those of the most used and consolidated simulators, even though it requires a reduced number of input

parameter. Furthermore, being an in-house code, it also guarantees a very high degree of flexibility and customization

for the analysis of specific problems.

3.0 Results and discussion

Several calculations with the benchmark building have been performed for different localities in Europe, in order to

calculate both heating and cooling energy demand. A selection of the main city analyzed are reported in Figure 5. The

figure clearly shows that heating demand is more significant than cooling demand, therefore the following sections will

mainly focus on the case of heating.

The present section reports the impact of different parameters on the heating energy demand of a reference building is

investigated for different countries, in order to understand which are the key drivers determining the energy

consumption of the building. In particular the effects of variations of thermal resistances, of window surface areas, of

orientation and of thermal capacities are investigated.

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3.1 Effect of thermal resistances

Figure 6 shows the consumption of the standard building described in Table 1, as a function of the heating degree days

(HDD), calculated with the MDDH method (Eq. 6):

365

1

24

1

,

24d dh

heb TTHDD (6)

in which Te,h represents the external temperature at each hour h, while Tb is the base temperatures for heating, which

represent the temperature set point of the inner heated zones. The HDD are computed starting from the hourly external

temperature profile of each locality, using a Matlab external routine.

The figure 6 reports the range of HDD examined for each country, therefore the consumption needs are estimated

according to the climatic and geographical parameters (i.e. longitude and altitude) of the single location.

The figure highlights that energy consumption for heating purposes is a linear function of HDD. An interesting

consideration can be drawn for the case of Italy, in fact Fig.6 highlights that Italy approximately covers all the climates

available in Europe. This is due to its geographical morphology, which covers all the climatic areas from the Alps to the

heart of the Mediterranean sea. As a result is very difficult to define in Italy a single plan of action to improve energy

efficiency all over the country, indeed it should be necessary to divide the whole territory into a relevant number of sub-

plans, well adapted to the requirements of each specific location in the country.

On the other hand, Fig. 6 shows that energy consumption of a given building can be well correlated to the HDD by

means of a linear equation, thus it is possible to conduct the analysis only for a few different values of HDD and then a

correlation for the intermediate points can be determined, in order to get the estimation of heating demand for different

locations, without losing too much in terms of accuracy.

Figure 7(a) reports the impact of the increase of external thermal resistance on the energy consumption for heating

purposes. The effect of an increase of the insulation substrate on the external side of the walls is taken into account. The

insulation thickness varies between 7 cm (i.e. base case) and 14 cm, therefore thermal resistance is in the range of

1.771-3.542 m2K/W.

Three values of increment of the resistance are considered and, as expected, a decrease of the consumption corresponds

to the increase of the resistance thickness, but the relative impact is stronger where HDD are low (i.e. warm climates),

rather than where HDD are high (i.e. cold climates), in fact the curves of Fig. 7(a) have a negative slope.

This effect might be due to two main causes: the first one is represented by ventilation losses, which are considered in

the model according to UNI EN 15251 (2008) and UNI/TS 11300-1 (2008), while the second one is represented by the

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losses across the windows. The relative impact for both of them is clearly much higher in cold climates, because

temperature of the external air is much lower, therefore their effect reduces the relative efficacy of the external

insulation layer, which just affects the thermal losses through the opaque walls . The effect of the dynamic approach

used in the present work is clearly highlighted in the non-linear profile, which can be noted in Figure 7.

Figure 7(b) shows the heating energy demand and the consumption reduction varying as function of the external

resistance in four localities with different HDD (Athens: 1169, Madrid: 2040, London: 2968, Berlin: 3250). It is

detected that in absolute terms, the increase of the resistance has a more relevant effect on buildings subjected to cold

climates (Berlin – London), while the relative impact is greater in warm climates (Athens), as already observed in

Figure 7(a). This happens because the energy necessary for heating purposes in cold climates is clearly much higher

with respect to warm climates because of the larger temperature difference and the lower radiation contribution,

therefore, even though the relative savings are higher in warm climates; the absolute saving of primary energy is much

higher in cold regions. These considerations are of particular importance when considering also the economical aspects

of the energy plans, given that the cost of insulation material are practically the same all over Europe.

Figure 8 reports the effect of the increase of internal resistance on the variation of energy consumption as a function of

HDD. It is shown that for cold climates the effect of the insulation tends to be smoothed for the same reasons discussed

for the external insulation case.

In this case the dynamic behavior of the building becomes fundamental in the analysis. Indeed internal insulation

decouples the internal environment from the wall of the buildings Kolaitis et al. (2013), therefore thermal inertia (i.e.

thermal capacity) of the walls cannot be exploited in the heating of the building and all the energy stored in the walls is

practically dispersed in the external environment, therefore more primary energy is required.

The insulation on the internal side of the walls forces a strong coupling of the wall with the external environment,

therefore the wall is “cold”, whereas in the case of external insulation the wall is coupled with the internal environment

and it results to be “warm”. In the case that the walls are “warm”, they act as a source of heat; in fact when temperature

inside the environment decreases, the walls transfer heat to compensate this decrease. In this way, the working hours of

the heating system are reduced.

On the contrary if the wall is “cold”, the effect of thermal inertia is lost and when internal temperature decreases, it is

necessary to activate the heating system to compensate the decrease, therefore the system is required to work for a

higher number of hours and a larger amount of primary energy is required. This is a well know effect in building

physics from a qualitative point of view, the importance of dynamic analysis by means of BEPS is that it is possible to

quantify numerically these effects, thus leading a quantitative energy plan evaluation.

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Due to the fact that these effects are strongly influenced by the usage profile, BEPS permits to perform analyses with

different profiles, in order to evaluate the energy demand with different operating conditions.

Figure 9(a) reports the average quantity of firing hours per start of the boiler in order to maintain the set point

temperature in the base case and for a double external insulation layer. For a double insulation thickness, a strong

decrease of firing hours is noticed, particularly for cold climatic conditions (i.e. Berlin). Instead, Figure 9(b) shows the

total number of starts per year of the boiler for the base case and for double insulation layer. It can be noticed that in the

case of double insulation the average duration of the starts is much lower with respect to the base case, but the number

of starts is higher. This indicates that the power of the boiler might be greater than the necessary (i.e. 7.5 kW in the base

case) and, in order to optimize the system, it could be decreased to have a reduced number of starting, avoiding the

impact of a high number of on/off cycles. A high number of on/off cycles cause the increase of maintenance cost and a

waste of energy due to each start-up of the system. The average duration of each firing period and the number of the

starts can be taken as a parameter to check the effectiveness of the boiler design for a correct dynamic building-plant

coupling.

3.2 Effect of windows surface area

The impact of the ratio between the surface of the windows and the total surface of the building is also investigated, in

particular the ratio value of the base case (i.e. 10.4%) is doubled and its influence on the heating energy demand is

analyzed as a function of the HDD.

In the present analysis, the distribution of the windows is uniform on all the surfaces; therefore effects due to the

asymmetric distribution of the glassed surface are not present.

The increase of the glassed surface causes two opposite effects: the increase of the radiation contribution, which tends

to decrease the heating energy demand, and the opposite effects to decrease the average thermal resistance of the

envelope, because windows have a reduced thermal insulation capability with respect to the opaque walls.

Figure 10 shows that for warm climates (i.e. less than 2000 HDD) the first effect is more important, whereas for cold

climates (i.e. greater than 2000 HDD) the second effect is prevalent. It is important to underline that also specific

parameters, such as the shadow degree (assumed equal to 0.7 in the present study), have a relevant impact on the effect

of an increase/decrease of glassed surface.

3.3 Effect of windows surface orientation

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If the distribution of the glassed surfaces is not uniform on the building surface, a different energy demand for heating

purposes is detected, according to the prevalent orientation of the windows.

Figure 11 highlights that if the windows are mainly concentrated (i.e. double surface with respect to the base case) on

the southern facade of the building, there is a decrease of the energy demand for heating, on the contrary if the

concentration is higher on the northern facade more energy is required. This effect is particularly relevant for warm

climates, whereas the effect in the cold climates is more limited (i.e. less than 10%).

Due to the influence of the solar radiation in cooling demand, there is the necessity to consider it, performing the

analysis in terms of total energy demand. Figure 12 and 13 report the total energy consumption (heating and cooling) as

function of the orientation. In particular, Figure 12 shows how the energy consumption changes with respect to the

orientation of the wall with the larger windows surface. Obviously, only in cold climates (as London) a larger windows

surface with a South orientation permit to reduce the total energy demand. In the other cases, the larger surface causes

an increase of cooling energy demand producing an increase of total energy demand. The asymmetry shown in the

figure is due to each local conditions in the year (temperature, radiation, wind direction and speed).

Figure 13 shows the same effect from another point of view; in fact it reports the decrease of variation of the energy

demand due to a change in the orientation of the window with respect to the South and it highlights the difference

among heating, cooling and total energy demand. The same trend as above is evidenced: for warm climates the north

orientation (180°) permits an energy saving, whereas other orientations causes an increase of the cooling energy

demand. This effect tends to be reduced for colder localities, as i.e. in Madrid where the South orientation permits the

greater energy saving. Also in London the South orientation represents the best choice, even considering the lower

impact of the cooling energy demand on the total.

3.4 Effect of thermal capacities (internal and confining walls)

The impact of thermal capacity of the walls and of the internal environment (i.e. air and furniture) on the heating energy

demand is also investigated. A higher thermal capacity tends to reduce the energy consumption of a building, if the

building needs a constant and continuous heating (i.e. hospitals), whereas if an intermittent heating is necessary (i.e.

offices, where heating is not required at night), it might not be useful to have a high thermal capacity, because the gain

achieved by not heating in one period is approximately lost when one has to heat more (i.e. part of the heat is absorbed

by the walls which store it), before people enter the building Karlssona et al. (2013).

In the present case, starting from the reference thermal capacity (Table 1), an increase and a decrease of one order of

magnitude is taken into account, in order to assess the impact on the energy consumption.

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Figure 14 (a) reports that the effect of thermal capacity is more relevant in warm climates (i.e. low HDD), this is

probably due to the fact that in cold climates, as previously discussed, the relative effect of ventilation losses is more

important with respect to warm climates, diminishing the effect of the thermal inertia of the walls.

In warm climates, the relative role of thermal inertia is more important and walls are able to store heat, which is

supplied to the internal environment when the internal air temperature decreases because of thermal losses to the

outside, determining a reduction of energy consumption, as highlighted in the Figure 14(a). Instead, the absolute

variation of the heating energy demand, as function of thermal capacity, is about the same for all the considered

localities, as shown in Figure 14(b).

This behavior might be explained considering that the variation of energy due to the increased thermal capacity is

connected with the wall temperatures. Thanks to the effect of external wall insulation, as previously stated, the wall

temperatures are mainly linked to the internal air temperature, which is constrained by the heating system in the dead-

band around the same set-point temperature for all locations. As a consequence, varying the thermal capacity has the

same effect independently from the location; on the contrary, location affects the absolute energy demand (i.e. in the

base case about 123 kWh/m2 in Berlin, 48 kWh/m

2 in Rome and 15 kWh/m

2 in Larnaca).

Figure 14(c) shows the effect of a change in thermal capacity on the energy consumption for three different locations. It

is detected that, in relative terms, the strong effect is present in warm climates and that a decrease of the thermal

capacity has a higher effect on warm rather than cold climates (Sadineni et al., 2011).

This outcome should be due to the fact that, in warm climates, walls can store solar radiation which is then released to

the internal environment when temperature decreases, and if thermal capacity is decreased this effect tends to vanish

(Al-Sanea, 2012).

Finally, it is necessary to consider the usage profile, which assumes an important role in this context in order to

determine the usefulness of the thermal storage: generally an high thermal capacity is useful for buildings with constant

usage profiles, which allows to utilize in a better way the energy stored in the envelope.

4.0 Conclusion

In the present paper a parametric investigation on the influence of some significant parameters (i.e. internal and external

walls insulation, window surfaces, thermal capacity and building orientation) on the energy consumption of a standard

building for different climates is reported.

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The analysis is developed in dynamic conditions by using a simulator developed at University of Genoa, (BEPS -

Building Energy Performance Simulator), which is validated by comparing its results with the ones coming from two

reference software, namely TRNSYS and Energy Plus, when applied to the same base test working conditions.

The analysis shows that a high potential is available to decrease energy consumption of the considered building.

Particularly, it is the effects of the internal and external insulation are calculated quantitatively and proven to be

extremely different, in fact in the first case (increase in the internal insulation) walls do not participate to the building

heating with their thermal capacity, whereas in the second case they give their contribution. Before deciding for internal

or external walls insulation, it is necessary to assess carefully the usage of the building under consideration, because if a

constant and continuous heating is necessary, then it might be convenient to have a higher thermal capacity. On the

contrary when a discontinuous heating load is present (i.e. schools, offices), the availability of a high thermal capacity is

questionable.

The influence of windows surface and building orientation is also taken into account. These two parameters influence

the amount of solar radiation received from the building and it is shown that their relative influence is more relevant in

warm rather than cold climates, where the intensity of radiation is higher. An increase of windows surface in cold

climates has the prevailing effect to reduce the resistance of the wall causing a dispersion of thermal energy to the

outside.

Finally, the effect of thermal capacity of the walls is considered, highlighting that its relative influence is stronger in

warm climates, where part of the solar radiation can be stored in the wall and furnished to the internal environment

when necessary.

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Figure Captions

Figure 1. Schematic of: (a) considered building, (b) all the thermal loads considered in the model; (c) heat transfer

contributions through external walls and (d) floor.

Figure 2. Block diagram of BEPS working flow

Figure 3. Comparison of the estimation of heating (a) and cooling (b) energy demand of our reference building for

different Italian cities.

Figure 4. Comparison of radiation values in kWh/m2 per year as a function of the orientation (H: horizontal): global

incident radiation in Rome (a) and Milan (b); direct radiation in Rome (c) and Milan (d).

Figure 5. Total energy demand for building heating/cooling for a selection of European cities.

Figure 6. Specific yearly heating energy demand as a function of the heating degree days (HDD).

Figure 7. Impact of increasing external resistance: (a) percentage reduction of energy needs as function of HDD; (b)

percentage and absolute reduction of energy needs as function of external resistance increase.

Figure 8. Effect of increase of internal insulation on the variation of energy needs as a function of HDD.

Figure 9. Average firing hours per start of the boiler (a) and number of starts (on-off cycles) per year of the boiler (b).

Figure 10. Specific yearly heating energy demand in the case of doubled window surface (to be compared to those of

the base case in Figure 6) and relative impact in terms of energy savings with respect to the base case (a negative value

means an increase in energy consumption).

Figure 11. Variation of heating energy demand as function of HDD, when the glassed surface is doubled for each

reference orientation. The mean effect on “All” surfaces, in diamonds, is the same as in Figure 10.

Figure 12. Variation of the total energy consumption as function of the wall with the larger window surface.. The

considered locations have the following HDD: Athens 1169, Naples 1429, Madrid 2040, London 2968.

Figure 13. Variation of the total energy demand with respect to the south orientation. The considered locations have the

following HDD: Athens 1169, Naples 1429, Madrid 2040, London 2968.

Figure 14. Analysis of the variation of walls and air thermal capacity on the percentage variation of energy

consumption (a); absolute (b) and relative (c) impact of thermal capacity on the energy consumption for three different

location. The considered locations have the following HDD: Berlin 3250, Rome 1627, Larnaca 808.

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Table 1. Main data concerning the considered building (Caputo et al., 2013)

General data

Height m 6

Base m x m 10 X 10

Number of floors - 2

Useful (heated/cooled) surface m2 200

Volume m3 600

Total dissipating surface m2 440

S/V m-1

0.73

Air exchange rate V h-1

0.5

Roof opaque surface m2 100

Roof windows surface m2 0

Type of floor on the ground

Vertical walls orientation N-S-E-W

for each orientation

Total Wall surface m2 60.00

Opaque surface m2 53.75

Windows surface m2 6.25

Table 2: Thermo-physical properties of each component (Caputo et al., 2013)

Transmittance

Specific

thermal

capacity

Absorbance

coefficient

Transmission

coefficient

W m-2

K-1

kJ m-2

K-1

- -

Vertical walls 0.40 622.92 0.6 -

Roof 0.35 395.28 0.6 -

Floor 0.42 320.65 0.6 -

Windows 2.465 0 0 0.571

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