CALCULATION METHODOLOGY

2.1 The establishment of climate zones in Europe

Given that the objective of the current task is to establish climatic zones in the European region in order to study the thermal behavior of alternative building formations, the use of degree-day approach was considered as the most appropriate method, since it can be used for a quick esti-mation of the heating or cooling energy consumption of a building, especially when the utiliza-tion of the building and the efficiency of the heating equipment can be assumed constant (Hit-chen 2013). The term heating (or cooling) degree days is defined as the positive deviation of the mean daily temperature T_m from a base temperature T_b, practically the outdoor ambient tem-perature, above which heating (or cooling) is activated to sustain the indoor temperature to a comfortable level:

The basic temperature depends on the constructional specifications of buildings and their ap-plication in research. For heating, the traditional degree-day or degree-hour procedure is based on a combination of theory and empirical observations and assumes that, on a long-term aver-age, solar and internal gains will offset heat loss when the mean daily outdoor temperature is 18.3°C and that energy consumption will be proportional to the difference between the mean daily temperature and 18.3°C. The applicability of this procedure is limited to residential build-ings, where envelope transmission and infiltration are the dominating factors contributing to the building load (Papakostas & Kyriakis 2005).

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Figure 1. The proposal for establishing climate zones in Europe on the basis of the heating and cooling degree days.

Within the context of this objective, the cooling and heating degree days of selected cities across Europe were calculated at a base temperature of 18°C. The cities were selected on the ba-sis of their population and availability of climate data. More specifically, all cities with a popu-lation over 100000 inhabitants were taken into account and among them, only 121 fulfilled the criterion of climate data availability.

Based on the climatic conditions prevailing on the selected European cities, a categorization into zones was proposed according to the region’s heating and cooling needs, which are de-picted by the number of Heating Degree Days and Cooling Degree Days. The value of 500 CDD was regarded as the threshold, above which the climate can be characterized as cooling dominated; the value of 2500 HDD (also found in literature) was regarded as the border for heating dominating climates. Lower values of HDD and CDD indicate lower energy needs for heating and cooling respectively. The proposed scheme is presented in Figure 1.

The cities with a cooling dominating climate (zones D and E) are located at the southern part of Europe and along the coastal regions of the Mediterranean. Climate zone C includes the se-lected sites from Portugal, where according to the climate data used in the analysis the climate seems to be mild, both during the winter and the summer periods. Zone B embraces the conti-nental areas of Italy and Spain as well as the coastal areas of northern France and Spain. The remaining cities comprise zone A and are characterized by their wintry weather. It must be men-tioned that a more analytic distinction could be made for the heating dominating sites; however, this research focused on the cooling performance of windows, which is not very essential for the overall building energy performance in cold regions (Tsikaloudaki et al. 2011).

2.2 Definition of the energy parameters

Cooling energy performance was assessed through the calculation of the cooling energy index, q_c, which represents the energy contribution of the rated window. It is calculated as the differ-ence between the annual needs for covering the cooling requirements of the referdiffer-ence room with the examined window system and the annual needs for cooling requirements of a notional room, which is identical to the reference one, but its window is adiabatic (U=0) and non transparent to solar radiation (g=0), divided by the window surface (Tsikaloudaki et al. 2012a, b).

Therefore, the energy index for cooling, q_c is derived as the difference between the cooling loads estimated for the reference room with the examined window on an annual basis, Q_c, and the cooling loads estimated for a notional room, which is the same as the reference room, but its window is covered with a material that prevents heat transfer (U=0 W/m²K) and admission of solar gains (g=0), Q_{c,no_win}, divided by the are of the window, A_w (ISO 2011):

The cooling energy needs of the reference unit, Q_c and Q_{c,no_win}, were calculated with the help of Energy+, an energy analysis and thermal load simulation program, which enabled the de-tailed dynamic analysis in every case. Energy needs are preferred to energy consumption, since there are various HVAC systems, which actually differ enormously from country to country.

The philosophy of this methodology is in accordance with ISO 18292, which mentions that “the window energy performance for cooling (EP_C,w) is expressed through the energy needs per unit area of the window per year that is the contribution of window to the energy needs of the refer-ence building for cooling” (ISO 2011).

2.3 The reference room

The cooling needs derived for each examined window were calculated for a reference room, the geometry of which is defined in EN 15265 (CEN 2007a) and ISO 13790 (ISO 2008). The reference room is of rectangular plan, 3.6m wide and 5.5m long, with a storey height of 2.8m.

This configuration was selected as it was assumed that both an office and a residential building could be formed by the multiplication of the reference unit.

For the analysis, all opaque building components of the reference room were regarded as adiabatic, with the exception of the front wall, which was regarded as thermally insulated with a 0.05m layer of EPS (λ=0.04 W/(m K)) positioned on its external surface. The window is located on the front wall and covers an area that varies from 10% to 99% of the façade.

Two usage profiles were taken into account; as regards office, it was assumed that it is occu-pied during the working days of the week (Monday to Friday) from 07:00 till 17:00 all year long. Only during this operating time the HVAC systems are in operation. The cooling and the heating set-points were considered equal to 24.5^oC and 22^oC, in accordance with EN 15251 (CEN 2007b). Internal loads were regarded equal to 132Wh per day, which account for 13.76W/m² during the operating time and 2 W/m² for the remaining time. Ventilation varied from 0.8ACH to 1.50ACH for Mediterranean countries, while for the rest of the Europe it was considered equal to 0.50ACH. In order to take into account the increased air permeability com-monly found in the Mediterranean structures, infiltration was considered equal to 0.50ACH for the specific zone and 0.2ACH for the rest of Europe.

For the residential usage, a full occupational status was taken into account. The cooling set point was considered equal to 26^oC in accordance with EN 15251 (CEN 2007b). However, it was assumed that the user would open the window when the indoor air temperature exceeded 24^oC with the condition that the ambient air temperature is lower than the one in the interior. In that case, ventilation was regarded equal to 2ACH. With closed windows a ventilation rate of 0.7ACH is required for air quality requirements. Infiltration rate was considered equal to 0.50ACH for the Mediterranean regions and 0.2ACH for the rest of Europe, in order to take into account the increased air permeability of the conventional structures found in the Mediterranean region. The internal thermal loads were considered equal to 5W/m².

2.4 The variables of the parametric analysis

For both cases (office and residential usage) alternative scenarios were studied, which differen-tiated mainly on the fenestration properties and the facades characteristics. As regards the fene-stration properties, window products with different thermal and optical properties with respect to their frame fraction were studied. The selected values for the thermal transmittance of the window U (Table 1) cover a wide range of conventional and advanced fenestration systems;

they range from 0.72W/m²K (passive window) to 3.20W/m²K. Both high (e.g. 0.76) and low (e.g. 0.30) values of the solar transmittance of the glazing g_gl were taken into account for each window (apart from the passive window). The solar transmittance of the whole window depends on the area of the transparent element; therefore the changes in frame fraction from 10% to 30%

resulted in different values of g, as presented in Table 1.

As regards the façade’s characteristics, different window sizes and orientations were studied.

It was assumed that the window was positioned on the main façade of the room and covered 10%, 25%, 50%, 75%, 90% and 99% of the façade. It was also assumed that the window and the façade faced the four cardinal orientations, i.e. they were orientated due South, North, East and West.

Table 1. The thermophysical and optical properties of the examined window types.

Window type U-window g-glazing Frame fraction g-window

W/(m²K) - %

The parametric analysis described above was conducted for 15 selected cities, which were representative of the zones described in chapter 2.1. For the coldest zone, the climate files of Aberdeen, Berlin, Stockholm, Tampere and Warsaw were included in the analysis. The conti-nental Europe was represented by Belgrade, Bilbao, Brussels, Madrid and Milan, while for sim-plicity reasons zones C, D and E (described in 2.1) were integrated in one and were represented by Athens, Larnaca, Lisboa, Malaga and Rome. The climatic data were retrieved from the ex-tensive database of meteorological information of Energy+. For most of the cities the IWEC (In-ternational Weather for Energy Calculations) database was used.

Additionally, two cases of thermal mass (heavy and medium) were taken into account for the analysis, in order to include the influence of thermal inertia on the cooling energy needs.

It is worth mentioning that for the office occupancy the results of 40320 simulations were used in the analysis, which accounts for 13440 cases for each climatic zone and 2688 for each examined city. For the residential use, the above mentioned numbers are reduced to half, as ven-tilation was not regarded as an independent parameter in the analysis. Totally 60480 simulations were run for the parametric analysis, resulting to a reliable and consistent sample for the statis-tical analysis.