In the lack of experimental data, one of the most elaborate building simulation codes, the TRNSYS software, was used to calculate the cooling loads for the selected residential
buildings. The use of such an advanced software (i. e. transient, multi-zone and interactive) was necessary as the reliability of simulation results strongly affects the performance of the, thereinafter, used ANN.

The weather inputs to TRNSYS include external dry bulb temperature, relative humidity, total and diffuse solar radiation on the horizontal and wind speed. The simulations were carried out for a period of one year, using the Typical Meteorological Year of Athens.

Five residential building are selected for this study. Table 1 presents some general details concerning these buildings. The building materials used in the models were based on the construction materials of each building.

	House 1	House 2	House 3	House 4	House 5
Volume (m3)	531	252	204	278	171
Floor area (m2)	183	87	71	96	57
Total wall area (m2)	146	79	64	84	46
Glazing area (m2)	22.8	12.86	9	12.74	4
Occupancy (persons)	4	4	1	1	2

Table 1. Details for the considered residential houses

In residential buildings there are some crucial factors, which are quite difficult to be measured or estimated, mainly due to inhabitant’s influence. In office or commercial buildings, the use and operation of buildings are in many cases well defined, making feasible to determine workings hours and estimate parameters like ventilation, lighting or other internal gains. Unlike this, residential buildings are highly dependent on the, often without a specific pattern, presence of occupants and more over on their personal habitudes, making the estimation of above factors a complex task.

This being the case, the validation of simulation results was thought necessary. To this effect, measurements of the indoor and ambient air temperature were performed during a period of two months, for April to May when heating and cooling loads are minimal and the building operate under free floating conditions. Figures 1 and 2 show the comparison of TRNSYS simulated and measured values, which are in very good agreement.

Fig. 1. Comparison of TRNSYS and actual values of indoor air temperature for buildings: House 1 and House 4

We will now quantify the performances of the glass collector relative to its various functions.

The evaluation of the glass collector characteristics, presented hereafter, comes from an extrapolation of test results carried out in 2003, on a first version [1], [2].

3.1: Heat insulation function.

The purpose here is to determine the Ug heat exchange coefficient of the glass collector. a) The Modeling Selected

Int.

_43_±_21 _ JL 15 Ё_1 1 ^4> * ! ^—1 " Fvt U 1__ J L_ —w 1 1 1 : Ui

.1. I I

-4,

Figure 2 : Illustration of selected modeling. These coefficients are given by the following relations :

Ui =

f vi 11 11

h———— Nu35 + hr35————- Nu62 + hr62 he

U2 =

V e ( 1

e

1

1

1

h 3‘X 3‘X 3‘X ь —— Nu35 + hr35————- Nu56 + hr56————- Nu62 + hr62

V e

(1)

(2)

Because of the heterogeneous composition of the glass collector, we modeled heat exchange between inside environment and the outside, considering three distinct zones as indicated on the diagram, figure 2.

r

U3 =

1

— • NU23 + hr23 e

(3)

he )

V

1 hi

1

SHAPE * MERGEFORMAT

hrij = 4 — a — Fij

ТЛ3

2

and Fij = 7

1

1

+ -1 Є; є j

ij From these exchange coefficients, one can determine Ug, the overall exchange coefficient of the glass collector, taking into account the respective surface of the various zones previously defined. We obtain then: ІUk • Sk U = (4)

with : hrij : radiative exchange coefficient between parallel surfaces express by :

b) U value estimation

The estimation was based on the thermophysical properties of the various components of the glass collector, as described in paragraph 2.2.

The parameters which mostly influence the Ug value are the surfaces emissivities and the thermal conductivity of included gas.

The value of various surfaces emissivities intervening in calculation are known with a good approximation and increase slowly during the time. Calculations were thus carried out with an emissivity equal to 0.05 for the absorber, to 0.04 for the low emissivity layer, position 3, and equal to 0.85 for the external glass, position 2.

The Ug value exchange coefficient according to the thermal conductivity of included gas, has a significant variation as shown on the figure 3.

If Krypton is used, the Ug coefficient is nearly 0.55 W/m2K, which positions the glass collector on the same level as the best triple glazing.

If the filling is air, the glass collector has an Ug coefficient equivalent to the double glazing filling with argon.

Knowing that the glass collector is designed to be installed instead of walls, the current version is manufactured with Krypton gas filling. The nature of the filling gas also influences the inside glass temperature, position 4, and consequently the solar factor. In order to reduce these values, a Krypton filling gas is recommended.

Next in the article, the performances of the glass collector are defined on the basis of the emissivity value specified previously, for a filling with Krypton.

Optimum daylighting conditions are always achieved with roof openings. The two cases presented here are shed and lantern type roofs as shown in Figure 10 and Figure 11, re­spectively. Due to the simple geometry, the number of factors is drastically reduced to 5. Besides the reflection coefficients, there are only geometric parameters whose meaning is explained in the figures. The results are presented in the usual manner in Figure 12 and Figure 13. Obviously, all parameters with positive effect on the overall daylight opening size show major positive influence. Internal reflecting parts of the ceiling show larger ef­fects than external reflection surfaces.

SHAPE * MERGEFORMAT

Figure 10: Geometry of the room toplit by roof sheds

Figure 11: Geometry of the room toplit by roof lanterns

II Л

SHAPE * MERGEFORMAT

Factor	Range of Def.	Mean	2 4 6 8 10 1	2
Min.	Max.	10.42%
1	X	0.20	0.40	0.56
2	R	0.10	0.20	3.38
3	Tf	30.00	60.00	1.08
4	Pi	0.30	0.70	0.59
5	Pa	0.30	0.70	0.17
	^eff	0.00	1.00	relative effect

Figure 12: Factors, definition bounds and resulting main effects on D for the case of the room toplit by roof sheds

Factor	Range of Def.	Mean 0	1 2 3 4 5 6
Min.	Max.	4.00%
1	X	0.20	0.40	0.49
2	R	0.10	0.20	1.29
3	a	0.10	0.20	0.46
4	b	0.10	0.20	0.47
5	Pi	0.30	0.70	0.32
	^eff	0.00	1.00	relative effect

Figure 13: Factors, definition bounds and resulting main effects on D for the case of the room toplit by roof lanterns

3 Conclusions and Continuation

Computer simulation is an extremely valuable experimental tool for studies with highly fluc­tuating boundary conditions as is the case in daylight studies with an always changing sky luminance distribution. Fixed sky conditions eliminate errors due to these natural fluctua­tions. The Design Of Experiments (DOE) method enables the restriction of necessary ex­periments to a minimum and a quick qualitative and quantitative analysis. The resulting polynomials are easy to program and quick to use.

With these first positive results, one can now plan to increase the number of factors in or­der to look at more detail in geometrical and optical definitions. Artificial lighting and its daylight dependent control could be investigated as well as integral aspects resulting from dynamical simulations like e. g. the daylight autonomy. Besides pure lighting analysis, it is possible to use the method for thermal building simulations as well.

[RAD] http://radsite. lbl. gov/radiance/HOME. html

[Sch97] Scheffler, E., "Statistische Versuchsplanung und Auswertung: eine Einfuhrung fur Praktiker", Dt. Verlag fur Grundstoffindustrie, 3. Auflage, 1997 [Sic03] Sick, F., "Einfluss elementarer architektonischer Maftnahmen auf die Tageslicht — qualitat in Innenraumen", Dissertation Universitat Karlsruhe, Faculty of Architectu­re, Fraunhofer IRB Verlag, 2003

The principle of face selective coating, as shown in figure 4, is based on the self shadowing effects of the structure. In PVD coating, the coating particles or vapour are propagating in a distinct direction. The angle of the particle beam (or the vapour) to the substrate is the free parameter that is

Fig. 6: SEM image of a replication of a 17 pm prism array in PS. The sample is partially coated. Here the area marked with „2“ is shown where the coating thins out.

Fig. 5: SEM image of a replication of a 17 pm prism array in PS. The sample is partially coated on the right edge. Here the area marked with „1“ in figure 4 is shown.

Fig. 7: SEM image of a 17 pm prism array in Fig. 8: SEM image of a10 ugm prism array in PS. The structure is coated face selectively PS. The structure is coated face selectively with a mixture of Mg and Ni. The „brighter“ with a mixture of Mg and Ni area is coated and one can see the sharp edge where the self-shadowed area begins

decisive of where the structure should be coated. We investigated how good a geometrical effect like shadowing will work for small feature sizes and for different types of coatings. Therefore, replications of a sample structure in polystyrene (PS) were coated with several materials. Afterwards the samples were examined with a scanning electron microscope (SEM). In figure 5 and figure 6, a magnification of the structure shown in figure 4 is represented. In this case a thick aluminium layer with approximately 120 nm was attached to the structure to be able to have a good look at the profile of the layer itself. Most of the layers which are usually be used, would have thickness of about 50 nm. In addition for using a SEM it is necessary to coat the sample with a thin metal film of about 20-50 nm. Therefore the separation of the second layer from the original coating would be difficult, if both layers would have equal gauge. A magnification of the marked area (1) of figure 4 is shown in figure 5. The layer is getting thinner at the edge until it nearly disappears in the shadowed area. The thin film (approximately 40 nm) that still can be seen in the shadowed area is obviously the layer of the second coating process for SEM preparation. Otherwise the intensity in the transmission measurements shown below (tab. 1) would be much lower (<5%) for normal incidence, because this film covers the whole surface of the structure. A magnification of the marked area (2) of figure 4 is shown in figure 6. There is a sharp edge between the shadowed and the non-shadowed area that is approximately 100 nm long until the coating virtually disappears. When using thinner films, it is much harder to resolve the coating in profile, but because of the different reflection properties of the coated and the non-coated area, it is still possible to separate them in the SEM. Figure 7 and figure 8, show two different samples of feature sizes of 17 pm (figure 7) and 100 pm (figure 8)

coated with a layer containing Ni and Mg of approximately 50nm thickness. This layer exhibits optical switching from a clear and slightly absorbing to a metallically reflecting state (switchable mirror)5. One can clearly see the sharp edge where the shadowed area ends and the coating starts.

The classical average method that was used at the beginning of the project of the full scaled PASSYS test cells (Vandaele, 1994) is based on steady state estimations; it has been applied as a first approach in this work. For this calculation the previously measured scaled cell data were used and the global properties of the overall heat loss coefficient, UA, and solar heat gain, gA, of the system were first calculated. Then these UA value and gA value were compared with the values calculated from the measurements of the solar irradiance, temperatures and electricity consumption for the small scale test cell, the classical average method was used to make a preliminary validation of the use of small scale test cell for the thermal evaluation of building components.

RESULTS

Figure 4 presents the measured results of the instantaneous electrical consumption in one day of the small scale test for the concrete slab and the clear glass, filter glass and reflective glass for a period of 13 hrs.

1200 1000 800 600 400 200 0

§

£ 0.0040 0.0035 0.0030 0.0025 0.0020

S’ 0.0010 0.0005 0.0000 1200 , 1000 ; 800 ^ 600 ■ 400 : 200 0

Time, hr

7:10 am

0.0015

21:50 pm

Figure 4. Measured solar radiation and instantaneous electricity consumption for the small scale test cells with three types of glazing.

Table 3 presents the overall heat loss coefficient, UA, and the solar heat gain, gA, of the small scale test cells for the clear glass, filter glass and reflective glass for concrete slab roof calculated from the measured results.

TYPE OF GLASS	UA	GA
Clear glass	10.04	0.64
Filter glass	9.16	0.68
Reflective glass	15.46	0.20

Table 3. Overall heat loss coefficient and the solar heat gain of clear glass, filter glass

and reflective glass for concrete roof slab

Figure 5 presents the measurements of the net heat loss divided by the temperature difference as a function of the incident solar radiation divided by the temperature difference using the classical averaging method. Linear regression technique was used, the intercept of the curve with the y-axis is the overall heat loss coefficient, UA, and the slope is the solar heat gain, gA. The least square equation is shown on the same figure.

Incident solar radiation divided by the temperature difference, W/K

-25 -20 -15 -10 -5 0 Incedent solar radiation divided by the temperature difference, W/K

(a) (b) -25 -20 -15 -10 -5 0 Incident solar radiation divided by the temperature difference, W/K (c) Figure 5. Measured points and fitted curves for the net heat loss divided by the temperature difference as a function of the incident solar radiation divided by the temperature difference: a) clear glass, b)filter glass and c) reflective glass for the concrete roof slab.

Table 4 presents the comparison of UA and gA for the classical averaging method with the ones calculated from the measured results of Table 3.

Table 4. Percentage difference between the averaging method and the measured results of UA and gA for three different glasses and concrete roof slab. Type of glass Averaging method Percentage difference, % UA gA UA gA Clear glass 13.26 0.75 24.0% 14.9% Filter glass 11.03 0.66 16.9% 2.6% Reflective glass 15.91 0.72 2.8% 72.2%

The results of Table 4 show that the smaller percentage difference was 2.6% for UA for the filter glass and 2.8% for UA for the reflective glass, followed by 14.9% for gA for the clear glass and 16.9% for UA of the filter glass. The higher differences were 24.0% for UA of the clear glass and 72.2% for gA for the reflective glass. These results indicate that the classical averaging method had disadvantages; it does not give information on the dynamics of the system because it was based on steady state equations (Vandaele and Wouters, 1994). Thus, in order to have a more confidence results, it is necessary to compare the measurements with a dynamic method, which could give information about
the effect of the short term weather variations on the system properties. However, to compare the measurements by using dynamic methods, such as identification parameter method, it is necessary to increase the number of measurements from hours to days.

SUMMARY AND CONCLUSIONS

This paper presented a preliminary study to validate scaled solar test cells for solar thermal evaluation of building components. The scaled solar test cells were described and their measurements were presented. The comparison between the thermal performances of the scaled solar test cells with the real scale test cells Passys was carried out by the classical averaging method that was initially applied to the Passys cells to determine the UA and gA. The results indicated that such comparison should be taken as preliminary validation results due to the fact that some percentage differences were high for some tests. Thus, we concluded that, it is necessary to continue the validation tests increasing the period of the measurements and by using dynamic methods such as LORD 2.2

The basis of the effects being observed is the Shockley surface states on semiconductor surfaces of metallised superinsulation screens and their interaction with
acceptor and donor gases in the process of well-known Bardeen-Brettain-Shockley gas-water cycle [32, 33].

Generally the conditions of atom existence on the surface of solids differ from the conditions, in which they are in a volume. The surface states are characterised by a greater probability of electron staying near the surface. They have their own energy levels differing from the volume states. The appearance of Tamm’s surface states [35] can be explained by means of the strong bond method (LKAO). Shockley [36] and Maue [37] have found another type of surface states known as Shockley’s levels. These states arise when free valencies exist on the surface. The Shockley’s chemisorption states appear only in case of a weak interaction between the adsorbent and the solid. It is this state that is characteristic to the Bardeen-Brettain-Shockley gas-water cycle [32, 33].

05

The hydrogen adsorption occurred on disperse surfaces of a dimension-quantised semiconductor film [6]. As the main model for the adsorbate placing on the adsorbent, the Peshev’s adsorbon can be successfully used. According to this model, the current carrier in the dimension-quantised film with an adsorbate consists of a conductivity electron (with x, y,z coordinates) and adsorbed molecules at the (y, z) surface section with the area of Л2. In the transverse direction, the electron is connected with the adsorbed molecules by an interaction depending only upon x, whereas along the у and z axes it moves freely changing its adsorption framing. It is assumed that the longitudinal and transverse movements of the carrier are separated, as it takes place in the film without an adsorbate. The difference is that the transverse part contains adsorbed molecules now and therefore determines the carrier mobility by its state. This state is just the Peshev’s adsorbon.

Wind Turbine

The 225 kW wind turbine has a hub height of 36m and a rotor diameter of 29m and is a Vestas V29 model previously in operation in the Netherlands. The turbine is connected to the buildings’ electrical distribution network and to the national grid. It is expected to generate 250MWh annually, which is greater than the anticipated building consumption, and excess power (equivalent to the needs of around 40 homes) will be exported to the grid.

Biomass

The buildings’ heating needs will primarily be met by a biomass boiler fuelled by the energy crop: Miscanthus or ‘Elephant Grass’, 5 hectares of which have been planted on the site. The crop is harvested annually in the late winter with conventional harvesting equipment and stored as bales until needed. The bales are shredded before being fed into the biomass boiler. The field is expected to yield 60 oven-dried-tonnes per year with a calorific value of 17GJ/tonne. The 100kW biomass boiler is provided by Talbott’s Heating.

It is 80 to 85% efficient and can modulate down to 25% of full load. The shredded bales are fed into the boiler by a mechanical screw auger. Biomass is carbon neutral as the CO2 emitted during combustion is balanced by the CO2 absorbed by the crop, which is coppiced on short rotation. The emissions from the boiler comply with the Clean Air Act. The boiler is expected to be installed and operating in 2004-2005.

Ground Water Cooling

Ground water is used to cool the buildings during the summer. Water is extracted from the local aquifer at 12°C via a 75m deep borehole. First it is used to cool and dehumidify the incoming air to the buildings in the Air Handling Units. The water is then circulated at 15°C through chilled beams (finned tubes) at high level in the offices. Finally, the water is used to irrigate the energy crop.

PVT Array

The 170m2 solar array comprises 54m2 of PVT panels and 116m2 of solar thermal panels. The PVT panels consist of a photovoltaic module, which converts light into electricity, and a copper heat exchanger on the back to capture the remaining solar energy. The panels have been developed by ECN in the Netherlands, incorporating Shell Solar PV elements and Zen Solar thermal elements. They produce electricity and hot water. The solar thermal panels are identical to the PVT panels, but without the photovoltaic element.

A one week measurement campaign was carried out in August 2003. The test room 1 was cooled by the radiant ceiling connected to the ground heat exchanger, while the test room 2 was left in free floating and used as a reference. The system operated uninterruptedly 24 hours a day with a mass flow rate of 520 kg/h in the ground loop and of 360 kg/h in the rooms loop.

The time evolution of the operative temperatures in the cooled and in the free-floating reference room are reported in Figure 2 together with the surface temperature of the radiant ceiling and the outdoor air temperature. The data show that the system was able to lower the operative temperature of 4-5°C with respect to the free floating situation. The total power demand of the two pumps was 91 W and the average thermal power extracted from the test room was 454 W. The coefficient of performance (COP), defined as the ratio between the removed thermal power and the electricity demand, is then equal to 5.