Energy demand modeling and forecast of Monoblocco Building at the city hospital of Genova according to different retrofit scenarios

Energy consumption is tightly linked to buildings and their intended use, as reported by Spyroupoulos et al. [1], with 39% of the total energy consumption in Europe ascribed to commercial and residential buildings. The final energy consumption in European non-residential buildings (NR-buildings) is dominated by space heating and cooling, electric equipment and lighting. In particular, electrical energy consumption has exhibited a constant increase over the last years due to the extensive use of HVAC and office equipment (electronic devices and computers) and is expected to increase from 42% in 2005 to almost 50% of the total energy consumption by 2030.

Moreover, Griego et al. [2] have given evidences of the fact that a correct optimization approach with an integrative energy analysis, can lead to a reduction of about 50% of the energy consumption in offices.

Energy saving and new technologies able to realize it are a key research topic, with many studies developed during recent years. Boyano, Hernandez and Wolf [3] proposed a methodology suitable for identifying what is the best combination of technical solutions in order to achieve the maximum energy saving. For instance, they observed that a high insulation is a best practice in cold and medium climates while for warmer climate the situation should be investigated case by case.

Retrofitting interventions have to be carried out with economical and comfort parameters in mind. Penna et al. [4] pointed out that some conventional energy efficiency measures allow to approach the zero energy target maintaining the economical convenience but worsening the indoor thermal comfort. In this perspective, they highlighted the importance of subsidies to sustain more smart but expensive solutions.

In the determination of the building energy consumption, Aksoezen et al. [5] showed that the building age of construction can be used as an indicator to roughly estimate the annual energy request by a building. The approach proposed can be very useful to address the intervention solutions as well, since it could facilitate a systematic improvement of the existing building stock.

Considering the retrofitting problem in a more complex way, Wu et al. [6] showed that it is possible to use a multi-objective neighborhood field optimization (MONFO) algorithm to find optimal retrofit strategies. The model takes also into account the possible maintenance costs of the selected interventions. The analysis showed that the algorithm is suitable to obtain accurate optimal solutions for energy efficient retrofit, and the maintenance strategy optimization can further improve the overall intervention performances. Vollaro et al. [7] investigated the differences in the results obtained from simulations run with a semi-stationary approach versus those obtained with a dynamic one. The studied building is located in the peripheral part of an historical city in central Italy. Results were validated with in-situ measurements of the thermal transmittance of the opaque walls by means of a heat flow meter and of the temperature field by means of a thermographic camera. The dynamic approach seems to be essential to deal with the inertial properties of the structure and to calculate the annual energy demand in an accurate way.

In the present paper, different retrofit technologies have been analyzed in order to evaluate the best intervention combination suitable for the retrofit of a case study building (the Monoblocco in San Martino city hospital). First, a list of Key Performance Indicators (KPIs) has been selected to identify the areas with most benefit from the retrofitting action. For the analyzed case study, external walls, windows and lighting system have been selected and arranged into intervention packages. The building have been dynamically simulated for each case in Energy plus environment and the results compared with the base case situation in term of energy consumption and simple payback period.

3.1 Geometry Drawing

The building 3D model has been created using the software Google Sketchup. In order to cope with the complex geometry of the whole structure of the building, the CAD plan view of each floor have been imported and then corrected in order to eliminate mismatches between outer surfaces.  

Once the raw model has been ready, it has been imported in Openstudio, which acts as a graphical interface that helps the compilation of Energy Plus input file (idf file). Then, the correct boundary conditions has been assigned  to every surfaces specifying if it is an external wall, an interior partition, a floor, a basement slab, in order to correctly establish which are the dispersing surfaces.

3.2 Glazed surfaces

According to Openstudio best practices, glazed surfaces have been included into the model by simplifing the windows geometry and using a built-in script (small utility programs written to help and speed up the modeling phase) to add fenestration via the definition of a wall to window ratio (WWR) (Table 1).

Table 1. Fenestration wall to window ratio

3.3 Shadowing surfaces

It is necessary to introduce into the model also the information regarding the shading surfaces and balconies. External shading surfaces alter the solar gains, influencing the energy balance, and are crucial in order to achieve energy savings.

Once the external shading surface are in place, it is possible to add also the windows blinds, that are controlled by means of a schedule, based on the solar radiation intensity: if the radiation is above a threshold, the blinds are unrolled down, otherwise they are kept rolled above the window.

3.4 Thermal bridge analysis

In a building, the contribution to heat losses due to thermal bridges is never negligible; in particular, in the analyzed building, due to the structure with balconies and overhangs, their contribution is very important.

Unfortunately, Energy plus does not provide the possibility to model and thus to take into account for them. Hence, for the calculations of the thermal bridges effects regarding the façades, this study refers to UNI EN ISO 6946:2008 and 14683:2008.

According to UNI 14683, the total heat flux dissipated by a wall can be calculated as:

$Q_{t o t}=U_{w a l l} \cdot A_{w a l l} \cdot \Delta T+\sum_{i=1}^{n} \phi_{i} L_{i} \Delta T$    (1)

given:

     - U_wall is the wall transmittance calculated with Eq(1);

     - A_wall is the wall area without windows [m²];

     - ΔT is the temperature difference between internal and external air, [K];

     - φ_i is the linear thermal transmittance of the i-th thermal bridge, [W/mK];

     - L_i is the i-th thermal bridge length [m];

The linear thermal transmittance depends on the type of the thermal bridge as well as on the wall stratigraphy, and their reference values can be found in UNI 14683.

For the windows thermal bridge analysis, the software WINDOW 6 has been used, a free program developed by Lawrence Berkley National Laboratory (LBNL) which contains a rich library with all the most common glazing manufacturer product data. This software allows to define the window (in dimensions, number of glass panels, air or gas filled gaps, frame material and dimensions, dividers and shading devices) in order to calculate the total transmittance, the light visible transmissivity and the Solar Heat Gain Coefficient (SHGC). Table 2 summarizes the calculated quantities.

Table 2. Glazing main properties

3.4.1 Example of calculation of thermal bridges for the south façade

The first step is to identify the modular element of the façade, in order to calculate the thermal bridges effect and then apply it to the whole surface (Figure 2).

Then it is possible to make a list of the wall stratigraphy, from external to internal side:

     - External painting;

     - Plaster (15 mm);

     - Hollowed tiles (250 × 100 × 250 mm);

     - Air gap (100 mm);

     - Hollowed tiles (250 × 100 × 250 mm);

     - Plaster (15 mm);

     - Internal painting.

2.png

Figure 2. South façade modular element

Given the stratigraphy of the wall, the transmittance U_wall [W/m²K] is calculated by using the approach of the thermal resistances [m²/KW] and considering the contributions of the different layers.

$U_{\text {wall}}=\frac{1}{R_{\text {vall}}}$   (2)

$R_{\text {wall}}=R_{s, i}+\left(R_{\text {plaster}}+R_{\text {bricks}}+R_{\text {tot}, \text {air}}+\right.+R_{\text {concrete}}+R_{\text {plaster}} )+R_{s, e}$    (3)

where:

     - R_s,i is effective internal thermal resistance  [m²/KW];

     - R_plaster is the conductive thermal resistance of the plaster [m²/KW];

     - R_bricks and R_concrete are the conductive resistances of, respectively, bricks and concrete [m²/KW];

     - R_tot,air is the total thermal resistance of the air gap, which comprises the convective and radiative contributions [m²/KW];

     - R_s,e is effective external thermal resistance [m²/KW];

R_s,i and R_s,e reference values can be found (for specified inner and outdoor conditions) in UNI 6946.

The total thermal air resistance R_tot,air have been calculated  according to UNI 6946 [33]:

$R_{\text {tot}, \text {air}}=\frac{1}{h_{a}+h_{r}}$     (4)

Considering an air gap with width d [m], h_a is the conductive/convective coefficient, calculated as:

$h_{a}=\max \left\{1.25, \frac{0.025}{d}\right\}$      (5)

h_r is the radiative coefficient, calculated as:

$h_{r}=h_{r, 0} \cdot E$      (6)

where:

     - h_r,0 is the black body radiative coefficient, presented in Equation (3) as function of temperature

     - E is a correction coefficient that takes into account the emissivities of the two surfaces constituting the air gap, e₁ and e ₂ respectively:

$E_{r}=\frac{1}{1 / \varepsilon_{1}+1 / \varepsilon_{2}-1}$     (7)

Table 3. Black body radiative coefficient for air gaps

Knowing the total heat flux (with thermal bridges included), calculated according Equation (1), it is possible to define a theoretical increased thermal transmittance of the wall U^*_wall:

$U_{\text {wall}}^{*}=\frac{Q_{\text {tot}}}{A_{\text {wall}}}$     (8)

Table 4 compares the values of the wall transmittance without and with considering the thermal bridges effect.

In the Energy Plus model this contribution is taken into account by properly increasing the thermal conductivity of one of the conductive layers of the composite wall of the module.

Table 4. North and South façade wall transmittance without and with thermal bridge contributions

3.5 Internal energy gains modeling

In order to obtain accurate results, a focal point is to correctly define the building internal gains. These are basically all the heating contributions (sensible or latent) that come from people, lighting and electrical equipment.

3.6 Thermal zones assignment

To correctly model the building, it is necessary to identify different thermal zones, according to ISO 13790. The zones are characterized mainly according to the intended use, keeping in mind differences in temperature set-points (Table 2), air changes per hour (ach) (Table 3), and internal gains.

3.7 Weather conditions

Weather conditions greatly influence the thermal behavior of the building and are contained in the so called weather file. This file includes all the information about the site where the building is located: its altitude, latitude and longitude, the climatic classification according to ASHRAE standards, data hourly series related to temperature, humidity, wind speed, solar radiation, precipitation and other important climatic parameters. This file also contains the “Design days” data, which are necessary to properly size the HVAC system.

3.8 HVAC modeling

Table 5. Temperature set points [32]

The modeling of the HVAC system has been carried out through the "Ideal Loads" option. With this option, an ideal unit is defined in order to supply an air stream at specified conditions. This unit ideally mixes air at the zone exhaust conditions with a specified amount of outdoor air and then adds or removes heat and moisture at 100% efficiency. The HVAC system is set up in order to respect desired conditions in terms of temperature (see Table 5) as well as air changes per hours (see Table 6).

Table 6. Minimum air changes per hour [31]

5.1 Base case scenario results

Table 8 summarizes the result aggregated on annual basis. Figure 3 reports the monthly break-down for heating and cooling loads. Cooling is necessary also during heating season because certain zones (i.e. operating rooms) with particular lights or electrical medical devices have significant internal gains.

In order to better analyze the results, the simulations provided also detailed values for every thermal zone, but for the sake of brevity, they are not included in this paper. 

Table 8. Base case scenario annual total energy required

5.2 Results for IP1: VIP facade, smart windows and LED system

Table 9. IP1 annual total required energy

Table 9 presents the annual values of energy required from the building in case of the retrofit intervention IP1.

From the results analisys it is possible to obtain the corresponding annual energy saving values with respect to the base case scenario:

     - Heating [MWh/y]: 190

     - Cooling [MWh/y]: 1329

     - Interior Lighting [MWh/y]: 2125

It is evident that the major saving is provided by the LED system installation. Lighting high efficiency solutions (like the coupled LED+daylight system) allow to reduce the effective comsumption, and also the internal gains linked to lighting systems.

The cooling is also greatly reduced, while the heating load seems not to change. This could be related to the fact that the major interventions are adressed to the south façade, that is mainly sun exposed and thus influences more the cooling load.

3.png

Figure 3. Base case: monthly heating and cooling demand

5.3 Results for IP2: VIP facade and LED system

Table 10. IP2 annual total energy required

Table 10 presents the annual values of energy required from the building in case of the retrofit intervention IP2.

From the results analisys it is possible to obtain the corresponding annual energy saving values with respect to the base case scenario:

     - Heating [MWh/y]: - 498

     - Cooling [MWh/y]: 1117.5

     - Interior Lighting [MWh/y] 2124

From the results, it is evident an increase in the heating demand (498 [MWh/y]); this result can be ascribed to the lack of the heat gain from the substitution of the fluorescent tubes that is not counterbalanced by the lower heat losses trough the opaque walls, that in turn constitute only some 50% of the overall facade surfaces. On the contrary, the presence of VIP material is beneficial during the summer, since it reduces the solar gain through the opaque walls.

5.4 Results for IP3: smart windows and LED system

Comparing these results with the ones relative to the base case scenario, it is possible to obtain these annual saving values:

     - Heating [MWh/y]: 463

     - Cooling [MWh/y]: 1111

     - Interior Lighting [MWh/y]: 2405

The situation is very similar to that obtained with IP1 (all the retrofit technologies applyed), but the amount of energy saved is smaller.

Table 11. IP3 annual total energy required

5.5 Results for IP4: Smart windows and VIP façade

The energy saving with respect to the base case scenario are the following:

     - Heating [MWh/y]: 930

     - Cooling [MWh/y]: 1119

     - Interior Lighting [MWh/y]: 0

There is no saving for the lighting since there is no intervention aimed to upgrade the existing system. The energy saving values are quite good, but the lack of LED illumination system is detrimental to the overall energy balance.

Table 12. IP4 annual total energy required

5.6 KPIs evaluation and retrofit analysis

For all the four simulated Intervention Packages IPs, a comparison with both the base case results and the benchmark values has been carried out. In the Table 13 the KPIs values of each case are summarized.

Table 13. KPIs comparison for the base case, retrofitted models and benchmarks