Population and the depth of shading system

growth and the increasing rate of building’s services and comfort levels, have
led to increase energy consumption in general. The reduction of energy
consumption in buildings can have a significant contribution in reducing global
demand for energy. The windows have an important role in energy efficiency of a
building. The Effects of window can be examined from two perspectives: The
first is heat transfer and second providing natural light. On the one hand
windows have less thermal resistance than walls and have the high Solar Heat Gain
Coefficient (SHGC), so
in the hottest days of the year they should have a minimum size. On the other
hand, due to the necessity of providing natural light and providing a part of
the building heating energy, solar radiation passing through the cold days of
the year demands the larger sizes of windows.

Many studies (Griego et
al (2015), Leskovar et al (2011), Gasparella et al (2011)) have carried out in
order to optimize the parameters of a building that make it better in the terms
of solely energy efficiency without considering other factors such as daylight
parameters. On the other hand, some have
been conducted to assess the effect of fenestration on the daylight and energy
consumption of buildings. However, these studies mostly have focused on
obtaining a single optimum solution not presenting a set of optimal results.

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what follows, the studies are performed through genetic algorithms (GA) which
provides the possibility of simulation based optimization. A fundamental issue
is that building envelope is complex entitle, and the variation of any
parameter related may alter the energy demand depending on the values of a

It has been emphasized
through many studies that the window dimensions and shading system has a
potential effect on the overall energy performance. For example, a methodology
was developed under the TRNSYS and Daysim program (Shan 2014) to determine the
dimension of window grid and the depth of shading system in order to find the
optimal solutions for the total energy demand, but it is limited to the single
optimum solution and the wide range of alternatives is not presented.
Echenagucia et al (2015) conducted a method by means of genetic algorithms, by
varying Number, position, shape and type of windows and the thickness of walls,
a multi-objective search was carried out in order to reduce the energy demand
for heating, cooling and lighting of a case study. both the absence and the
presence of an urban context in the climates of Palermo, Torino, Frankfurt and
Oslo were considered to drive the analyses. In all the climates, for the
analyzed case study, a small overall Window-to-Wall Ratio (WWR) of the building
was shown by the results.

Fasi et al (2015)
predicted the energy while Lighting energy and cooling energy consumption were
the parameters used to assess it and visual performance while glare index and
Daylight Factor (DF) were used to determine it for three different types of
glazed windows in the office building. In the annual cooling and building
energy consumption a noticeable reduction was observed for all types of modeled
windows while visual comfort was not provided in all cases.

The configuration of
the facade can affect three terms of the annual energy demand of a building, as
defined in EN 15603 (Energy performance of buildings) the energy need for
heating (EH), the energy need for cooling and dehumidification (EC), the energy
need for lighting (EL). The other three terms of the total energy demand of the
building i.e. energy need for ventilation and humidification, hot water and
other services are not directly affected by the configuration of the façade
(Goia 2013). This contradiction shows Choosing their areas
and proportions is part of fundamental early design stage decisions, which are
hard to change later. Therefore, window dimensions must result from a careful
process and be part of an integral design process, considering multiple aspects
at the same time (Ochoa 2012). Although optimization can
be used to achieve an optimal solution, however the main goal should be how to
use energy and lighting standards in order to achieve a balance of both factor.

Goia et al (2013) by developing a work flow the performance
of Integrated thermal-daylighting simulations on a low energy building were assessed.
Providing the optimal WWR of the envelope which reduces the total energy
consumption in a temperate oceanic climate was the goal of this study. The
results show that, regardless of the orientations and area of façade, when the
WWR is between 35% and 45% of the total facade module area the optimal
configuration is achieved. In this range, daylighting conditions were met the
qualified level. so, in early stages of design this range of WWR can be
considered as an optimal one. little dependence of Etot (WWR) on the
building geometry and the HVAC efficiency was shown by analyses.

Goia et al (2013) developed a process by which the
Integrated thermal-daylighting simulations on a low energy building were
performed. The aim of this work was to find the Optimal WWR of the facade that
minimizes the total energy demand in a temperate oceanic climate. The results
show that, regardless of the orientations and of the facade area of the
building, the optimal configuration is achieved when the transparent percentage
is between 35% and 45% of the total facade module area. In this range,
daylighting conditions are also satisfactory and this WWR can therefore be
considered a good starting point in preliminary design phase. The analyses show
little dependence of Etot (WWR) on the building geometry and the
HVAC efficiency.