precis: assessing the potential for renewable energy in...

PRECis: Assessing the Potential for Renewable Energy in Cities

THE EUROPEAN COMMISSION Directorate General XII Science, Research and Development

Joule III Non Nuclear Energy Programme

Contract JOR3-CT97-0192

Solar and Daylight availability in urban areas

Final Technical Report

Feb. 1998 to Jul. 2000

Dr Raphaël Compagnon

Ecole d’ingénieurs et d’architectes de Fribourg 80 Bd. Pérolles, 1705 Fribourg, Switzerland

Tel.: +41 26 429 6611 FAX: +41 26 429 6600

E-mail: [email protected]

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY.............................................................................................................................1

2. OBJECTIVES AND SCOPE OF PARTNERS' INVOLVEMENT .............................................................................2

2.1 Brief state-of-the-art of partner's involvement .........................................................................2

2.2 Problem definition ......................................................................................................................2

2.3 Methodology of investigation....................................................................................................4

3. SCIENTIFIC AND TECHNICAL DESCRIPTION ................................................................................................6

3.1 Evolution of tool development ..................................................................................................6

3.2 Identification of issues to be tackled ........................................................................................7 3.2.1 DOSKYMODEL .....................................................................................................................7 3.2.2 PPF......................................................................................................................................11 3.2.3 PPFCALC............................................................................................................................13 3.2.4 Output data.........................................................................................................................13

3.3 Tool application on case study sites ......................................................................................20 3.3.1 Generic case study sites...................................................................................................21 3.3.2 Existing case study sites ..................................................................................................21

3.4 Parametric simulations ............................................................................................................23

4. RESULTS .............................................................................................................................................24

4.1 Presentation of results.............................................................................................................24 4.1.1 Generic case study sites...................................................................................................25 4.1.2 Athens case study sites ....................................................................................................26 4.1.3 Grugliasco case study sites .............................................................................................27 4.1.4 Perolles case study site ....................................................................................................28 4.1.5 Sensitivity to rotation of the Pérolles site .......................................................................31 4.1.6 West-Cambridge case study site......................................................................................32 4.1.7 Trondheim case study site................................................................................................33 4.1.8 Urban canyon case study .................................................................................................34

4.2 Discussion ................................................................................................................................40

5. CONCLUSIONS .....................................................................................................................................43

6. REFERENCES.......................................................................................................................................43

7. ACKNOWLEDGEMENTS..........................................................................................................................47

8. ANNEXES ............................................................................................................................................48

8.1 Glossary ....................................................................................................................................48

8.2 Climatic data summary ............................................................................................................49

8.3 Part geometry............................................................................................................................50

8.4 Irradiation, illuminance and daylight factor distributions .....................................................51 8.4.1 Athens case study sites ....................................................................................................51 8.4.2 Grugliasco case study sites .............................................................................................52 8.4.3 Perolles case study site ....................................................................................................54 8.4.4 Sensitivity to rotation of the Pérolles site .......................................................................57 8.4.5 West-Cambridge case study site......................................................................................58 8.4.6 Trondheim case study site................................................................................................59


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1. Executive summary

"Solar cities" is an emerging concept which attempts to demonstrate that passive and active solar techniques are able to contribute significantly to the energy balance of urban areas. Optimised penetration of daylight into buildings is also proven to have an important potential for reducing electricity consumption. For such applications, the related solar radiation exploitation systems could be positioned on building facades as well as on roofs.

To assess the energy contributions that can be expected from these natural resources (the sun and

the luminous sky vault), the irradiance (in [Wm-2]) and illuminance (in [lx]) falling on building envelopes (facades + roofs) have first to be quantified. However, high building densities usually encountered in the urban context rather complicate this task. Two issues are of particular importance: the dynamic shadow patterns due to neighbouring buildings and interreflections between building facades.

This report presents a method to address these issues by numerical simulations. The resulting tool

comprises four distinct components: • a method to produce accurate anisotropic average sky models from meteorological data; • a dedicated program to handle simplified geometrical descriptions of the urban areas under

investigation; • a set of automated procedures to perform the numerical simulations using RADIANCE, a ray-

tracing program originally developed to model light propagation within buildings; • a performance analysis scheme based on irradiation and illuminance distributions over the

building envelopes. In addition, the report presents results from applications of the method to several “virtual” and real

urban case study sites.


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2. Objectives and scope of partners' involvement

2.1 Brief state-of-the-art of partner's involvement

Our participation to the PRECis project stems from our long experience in using the RADIANCE backward ray-tracing program [Com93] [Com94] [Cla96] [Com97]. Originally conceived as a rendering program [Lar98], RADIANCE can also produce physically meaningful numerical results. Thus, it is extensively used for lighting and daylighting simulations and has been well validated. In addition, its modular structure as well as its largely adjustable calculation procedures makes RADIANCE usable for many other applications.

Since solar radiation and visible light obey the same laws of physics, it seemed logical to also apply

RADIANCE to compute solar irradiance in the built environment. Our first experience with this type of application was conducted in 1993 at the LESO-PB in Lausanne (Switzerland). At that time we computed the global solar irradiance falling on photovoltaic arrays positioned in a roof close to a large obstruction by simulating a “virtual sky” containing as many suns as diurnal hours. By doing so, it was possible to get the annual irradiation by a single simulation run. This idea has been further developed in the present project.

While terminating this project, it appeared that the use of RADIANCE for the accurate calculation of

irradiance or illuminance in urban areas is currently also investigated by other researchers [Sch98a] [Sch98b] [Wag98] [Mar00]. This is clearly a positive sign.

2.2 Problem definition

The proper orientation of buildings in urban areas is a problem that has already been addressed by several methods whose objectives have changed over time. A brief outline of this history, which still remains to be studied in full details, is given hereafter.

Ancient cities were often built around axis aligned with some spatial reference lines defined for

instance by the direction of the sunrise that occurred the day of the foundation. This type of urban planning was probably more dictated by ritual needs than by climatic considerations. However, as it appears in Vitruvius’ books, the climatic implications of building orientation within a city texture and their variations with latitudes have also been early recognised. One of the main issues was to provide adequate sun penetration in order to prevent illness. An old Italian proverb illustrates very well this point: ‘Dove non va il sole, va il medico’ or ‘Where the sun does not go, the doctor does’ [Hob99]. In medieval cities this kind of hygienic considerations were often contradicted by legislation that introduced taxes based on window sizes.

As the end of the 19th century brought scientific evidences that sunlight has a bactericidal property,

building access to sunlight regained interest. Several attempts to translate these findings into planning standards or regulations began around 1900 in USA [Moo81]. The provision of daylight into urban canyon also became a topic that, for instance, informed New York zoning regulations issued in 1916


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[Hey29] [Las90] [Rid90]. At that time, similar studies were also conducted in Europe. In France, the “heliothermic axis” theory [Rey28] that prescribes to align buildings on a NNE-SSW axis seems to have deeply influenced Le Corbusier although its scientific basis is questionable [Bar45].

Increasing living standards and the rapid development of building services (e.g. central heating,

electric lighting) made solar and daylight access less regarded issues until the first oil crisis in 1973. However, it has been soon recognised that the amounts of energy used in cities and eventually released as heat (i.e. anthropogenic heat) are of the same order of magnitude of the incoming solar radiation [Win76]. Since then several studies have been devoted to the urban microclimate and its relations with energy consumption [Sch84] [Oke88] [Eli91] [Esc91].

In the meantime, solar and daylight penetration into buildings has been studied in the light of

thermal and visual comfort. Some intuitive knowledge about user preferences were backed up and refined with large scientific surveys [Gra73]. Some of their findings served to define standards that are still in use (e.g. the German norm DIN5034 “Tageslicht in Gebaüden”). Their focus is more on individual buildings for which explicit details are prescribed (e.g. window size, rooms’ layout relatively to facade orientations) and less on urban planning regulations that are just implicitly addressed (e.g. by specifications regarding minimal sunshine duration). A notable exception is the “solar envelope” method developed by Knowles in the mid 1970’s [Kno81], which clearly addresses urban planning issues.

Since the 1980’s the increasing awareness for the potential of energy efficiency measures to

mitigate adverse effects of pollution as well as the international commitment for a sustainable development obtained in Rio in 1992, has encouraged the integration of renewable energy applications at a large scale directly within city textures. So called “solar cities” where the energy supply would be largely, if not totally, provided by solar techniques are now seriously taken into considerations. Hence, the recent need for evaluation tools to assist the design process and to quantify the potential of these techniques in this particular context.

At this point, currently available tools can be separated in three broad categories: the methods

based on geographical information systems (GIS) or photogrametry, those which are solely based on sunpath geometry analysis methods and, finally, those which incorporate solar or daylight quantification methods sometimes based on meteorological data.

Several recent developments belong to the first category [Wit97] [Gut98] [Ryl00]. These are mainly

designed to study the potential for renewable energy applications in existing urban areas. The second category includes the “solar envelope” method and its more recent developments [Sch93] [Kno00] [Cap00] as well as those that use sunpath diagrams with various kind of geometrical projections [TelXX]. These are mainly dealing with direct solar radiation and their goal is to provide controls of the overshadowing between neighbouring buildings.

The last category contains several recently developed tools [Yez94] [Sha97] [Nie96] [Car98]

[Mor98] [Qua98] [Sch98a] [Sch98b] [Wag98] [Mar00]. Their use enables to quantify the amount of solar radiation that reaches a particular location on a building envelope. This is a crucial information when trying to assess the energy contribution that can be expected from solar penetration in an urban area. The purpose of this work is to develop such a tool especially adapted for studying solar and daylight access in urban areas (i.e. that can provide results for large numbers of buildings).


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2.3 Methodology of investigation

Assessing the final energy that can be produced by solar applications or saved by daylighting strategies in an urban area requires four steps:

1. Climatic characteristics of the site have to be processed in order to build representative radiometric and photometric average sky models. The former is required for all solar energy applications while the latter is used for assessing daylight contribution to the lighting of buildings.

2. Geometrical characteristics of the buildings, site ground relief and additional parameters such

as facades glazing ratios and surfaces reflectance have to be incorporated in a 3D model.

3. The two models must be processed by a computer simulation tool to calculate irradiance and illuminance falling on building envelopes.

4. Solar collectors and daylighting systems have to be positioned at specific locations and their

contributions to the related buildings have to be calculated by specialised building energy simulation tools. At this later step technical and constructive details of buildings as well as occupants behaviour play an important role that can finally be represented as “Utilisation factors”.

The relationship between the four steps presented above can be expressed as a symbolic equation

where the ≈ symbol denotes an operation performed by a computer simulation tool:

[Final Energy] = [Urban Solar and Daylight Availability] ≈ [Utilisation Factors]

with:

[Urban Solar and Daylight Availability] = [Sky Model] ≈ [3D Buildings Model]

Since the main focus of this project is to study the effects of buildings geometry, layouts and orientations on the potential for renewable energy in urban areas (i.e. passive and active solar thermal applications, photovoltaic modules, daylighting strategies), this work concentrates on the second equation which involves steps 1) to 3) only.

At this latter step, the accurate simulation of the multiple reflections occurring between

neighbouring buildings largely affects the results. Former studies have usually dealt with this issue using simplified methods involving some kind of “obstructions and orientation factors” that were finally multiplied by unobstructed irradiance and illuminance values. These factors are assumed manually, or calculated using various specialised software, such as OMBRE-URBAINE [Car98], LESO-SHADE [Mor98], SOMBRERO [Nie96], SHADING [Yez94] [Sha97], SUNDI [Qua98] and TOWNSCOPE [TelXX]. All these software make use of a rather simplified geometric model of the existing buildings. Their main limitations reside in two aspects: • the assessment of diffuse irradiance is approximate and is often considered as isotropic; • interreflections between ground and buildings are generally omitted [Lit98].


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The present study aims to assess the urban solar and daylight availability much more accurately with few restrictions on the geometric complexity of the urban forms under investigation. The core part of the study lies in the extensive use of the RADIANCE ray-tracing program, which has been originally developed to model light propagation within buildings [Lar98]. Its physical accuracy has already been proved by several studies [Com94] [Mar97] [Ubb98]. Its main advantage lies in its specific ray-tracing algorithm that can account for multiple reflections between buildings and that can deal with any kind of radiance or luminance distribution over the sky vault.


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3. Scientific and technical description

3.1 Evolution of tool development

Figure 1 shows the overall structure of the tool issued from this project. It comprises several components that were programmed for computers running under standard UNIX systems on which RADIANCE has also been originally developed. So far, the tool has been successfully tested on SUN SPARC-stations, Silicon-Graphics workstations and PC’s equipped with a LINUX system.

Figure 1: Overall structure of the tool. The components that have been specifically developed for the PRECis project are shaded in grey. Arrows indicate data flows between modules. RADIANCE is used at three places as a calculation engine.

The PPFCALC module controls all calculations and output data formatting. The remaining

components serve to feed this module with data in the required formats: sky models are prepared by the DOSKYMODEL module and 3D building models are generated by the PPF program. The latter can import data formatted as DXF files through the DXF2PPF converter. The functions of these modules and the methods they use are explained with more details in section 3.2.


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3.2 Identification of issues to be tackled

3.2.1 DOSKYMODEL

The first issue is to build appropriate sky models. The DOSKYMODEL program has been developed for this purpose. It uses an existing and well-validated sky model developed by Perez & al. to obtain either radiance (in [Wm-2sr-1]) or luminance (in [cd m-2]) distributions on the sky vault [Per90] [Del95]. These parameters are computed as monthly or annual mean values. The climatic data required by the Perez model (hourly direct and diffuse irradiance values) are obtained from the METEONORM software [Kun95]. To reduce the amount of data, distributions are calculated using the standard subdivision of the sky hemisphere into 145 zones as defined by the CIE (Figure 2). Even the direct component is distributed among these zones.

Figure 2: Stereographic projection of the sky vault subdivided into 145 zones as defined by the CIE. North is oriented towards the top, East towards the right. The algorithms required to perform the subdivision are presented in full details in [Tre93].

The DOSKYMODEL program is able to generate these statistical sky models for any relevant time

intervals from weather files produced by METEONORM. For solar energy applications, sky models include the values for all diurnal hours. On the other hand, when dealing with daylighting strategies, only diurnal working hours are considered since potential electricity savings can occur solely when buildings are occupied. The calculation method used by DOSKYMODEL is briefly explained hereafter.

First a set of 8760 (=365x24) hourly direct normal solar irradiance Gb(t) and horizontal diffuse

irradiance Gd(t) values are generated by METEONORM for the location under study. These values are then poured into the Perez model to produce radiance or luminance distributions FPerez(Gd(t), Gb(t),d) for each hour t. These distributions being continuous over the sky vault, they are sampled in order to obtain 145 radiance or luminance values. For each zone k, a value vk(t) is computed by taking 10 samples of the distribution:

( )idbi

Perezk dtGtGFtv ),(),(101)(

10

1∑

=

=


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where di are random directions distributed within the sky vault region covered by zone k. A specific module of RADIANCE developed in a PhD thesis [Del95] is used for this purpose. Since the distribution accounts only for the diffuse component, the direct component due to the sun’s disk has to be processed separately. The sun direction is first calculated to find in which zone ks it is located. Then the direct component is added to vks(t) in order to obtain a final value Vks(t) for the zone ks:

)()'(

)()()(ksdiskssun

tRtvtVp

psksks Ω

Ω+=

where Ωp are projected solid angles (measured from a horizontal plane) of the sun’s disk and the zone ks respectively and Rs(t) is the true radiance or luminance of the sun’s disk obtained from Gb(t). This process “dilutes” the sun’s disk radiance or luminance over the entire zone. To avoid under-sampling artefacts due to the hourly displacement of sun’s direction, this process is applied at 10 random time intervals for each hour t.

At this stage, horizontal irradiance G(t) and illuminance E(t) can be computed using the set of Vk(t)

values:

)()()(or)( ktVtEtG pk

k Ω⋅= ∑

Figures 3 and 4 show a comparison between these calculated values and the horizontal global irradiance and illuminance given by METEONORM. As it appears, the model very well reproduces irradiance values. This gives a first validation of the procedure explained above. For illuminance values, a slight bias is observed. However, since the illuminance values generated by METEONORM are also computed using a built-in algorithm, this bias just highlights differences between this algorithm and our model.

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Modeled global irradiance [W/m2] Figure 3: Hourly horizontal global irradiance values G(t) computed using the 145 zones model compared to the values generated by METEONORM for the Fribourg climate.


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Modeled global illuminance [lx] Figure 4: Hourly horizontal illuminance values E(t) computed using the 145 zones model compared to the values generated by METEONORM for the Fribourg climate.

Once the series of sky zone sample Vk(t) is available, specific sky models can be produced by

computing relevant average values wk for each zone:

∑∑ ⋅

=

t

tk

k tf

tVtfw

)(

)()(

where f(t) is a function defined in order to select the appropriate time intervals. Note that f(t)=0 during night hours. In this project three different sky models have been used. The first is devoted to evaluate the potential for passive solar energy applications during the heating season. Therefore the required sky model is calculated with radiance values taken over the heating season. This selection is operated by function fpa(t) defined as:

otherwise0;12if ANDhoursdiurnalincomprisedif1)( CTttf extpa °≤=

where Text is the daily mean external temperature. This simple method of defining the heating season does not include any parameter that depends on the buildings under study. Although it certainly overestimate the heating season length for well isolated buildings, it has been chosen to be applicable for existing urban areas that still comprise many old buildings. A complementary function f’pa(t) can be defined to select the remaining hours of the year in order to build a “summer season” sky model:

otherwise0;12if ANDhoursdiurnalincomprisedif1)(' CTttf extpa °>=

The second sky model is made to evaluate the potential for photovoltaic (PV) applications. It is calculated using radiance values for all diurnal hours. Therefore fpv(t) is simply defined as:

otherwise0;hoursdiurnalincomprisedif1)( ttf pv =

The third sky model is build to study buildings access to daylight. Hence, the model is based on luminance values Vk(t) with the function fdl(t) defined as:

otherwise0;68if1)( pmtamtfdl ≤<=


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The time interval 8h-18h is defined to embrace the full working day where daylighting applications may significantly reduce artificial lighting needs. If required these limits can be changed within DOSKYMODEL. Time t being defined as legal time, a “summertime period” is taken into account between March 26th and October 29th.

Average radiometric sky model computed over the heating season (2299 hours):

Average radiometric sky model computed over the summer season (2031 hours):

Average radiometric sky model computed over the whole year (4330 hours):

False colour radiance scale:

Figure 5: Average radiometric sky models computed for Fribourg.

Average photometric sky model computed over the whole year (4330 hours):

False colour luminance scale:

Figure 6: Average photometric sky model computed for Fribourg. The slight disymmetry around the vertical North-South axis is resulting from the shift due to the “summertime period” between March 26th and October 29th.


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Figures 5 and 6 show the resulting average radiometric and photometric sky models obtained for the climatic conditions prevailing in Fribourg. It clearly appears that the brighter regions of the sky vault are those crossed by the sunpath. The remaining regions that contribute to the diffuse irradiance/illuminance are not totally uniform either. Averages sky models computed for the other case study sites are shown in annex 8.4. If required, the direct and diffuse components can be separated (Figure 7).

Figure 7: Stereographic representation of radiance levels for the Fribourg radiometric average sky (left: diffuse component; centre: direct component; right: global radiance. Radiance levels are averaged over 4330 diurnal hours for a whole year. 3.2.2 PPF

From the beginning of the project it appeared that a common format for describing and recording the models of the case study sites was absolutely necessary. Instead of using an available GIS tool that had to be adapted to the project specific needs, it has been decided to develop a proper format along with the program (named PPF) that handles it. This choice was motivated to preserve full flexibility about the kind of elements introduced in the models. Also, the largely varying nature of the raw data sources available for each case study was a further argument to undertake PPF development. Nevertheless, because the format relies on a very simple ASCII data representation, if required the models could easily be transferred into other GIS programs

The objectives of the project being defined as the evaluation of “potential for renewable energy”,

PPF does not require to model buildings with all their constructive details. Hence, a simplified geometrical description has been defined in order to retain buildings positions and shapes but excluding small-scale constructive details on the facades (see figures in annex 8.3):

• A building is made of one or several parts that form basic geometric entities. • A part is defined by a polygonal outline (i.e. its footprint on a land use register map) and a

height. Its resulting geometric shape is a prism with flat top. By using several parts, a single building with complex shape can be modeled.

• Tilted roofs can be modeled using two heights (at eaves and top levels) and a “tilted roof distance” (i.e. horizontal distance measured on a plan between the top and the eaves).

• For each part the number of floors which is required for computing its gross surface floor area can either be introduced manually or estimated from parts heights.


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• If the site is not flat, its topography is described by altitude values assigned to regularly spaced points on a rectangular grid of 10 to 50[m].

Some of these data (typically building footprints) can be extracted from DXF files using the

translator program DXF2PPF. Floor and facade surface reflectance as well as glazing ratios have a significant effect on the radiation penetration in the urban texture and therefore should be taken into account. However these parameters do not directly depend on urban form and their on-site measurement in a real case study area would require a huge effort. Therefore, a constant reflectance value ρ=0.2 that includes the effect of glazing ratios is assumed for both radiometric and photometric simulations.

Once the model is fully described into PPF, various functions allow data editing or saving and the

generation of the input files required by PPFCALC. Some statistical figures can also be computed directly within PPF. Figure 8 shows two models produced by PPF with their corresponding statistical figures as computed by PPF.

Perolles SITE AREA: 117328 [m2] GROUND FLOOR AREA PER UNIT SITE AREA: 0.31 [m2/m2] BUILT URBAN VOLUME PER UNIT SITE AREA: 4.25 [m3/m2] # OF BUILDINGS: 61 PLOT RATIO: 1.23 [-]

Plaka SITE AREA: 270014 [m2] GROUND FLOOR AREA PER UNIT SITE AREA: 0.48 [m2/m2] BUILT URBAN VOLUME PER UNIT SITE AREA: 4.98 [m3/m2] # OF BUILDINGS PARTS: 1982 PLOT RATIO: 1.58 [-]

Figure 8: Two models produced by PPF and rendered with RADIANCE. Far top: Perolles area in Fribourg (Switzerland) as viewed from the south-west direction. Top: Plaka area in Athens (Greece) as viewed from the South direction. The Acropolis hill is visible at the lower left corner. Statistical figures refer only to the buildings located within the studied areas as shown in section 4.1.

PPF can also produce DEMs (Digital Elevation Models) that represent a site map view of the model where building heights and ground altitudes are expressed in pixel values of scalable size (usually 0.5 to 1[m]). Various image-processing techniques are then applied on these DEMs in order to extract a series of parameters or descriptors that characterise the urban fabric [Ste97] [Ste98] [Rat99]. The LT-Urban tool also developed within the PRECis project performs such functions [Rat00].


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3.2.3 PPFCALC

Although quite straightforward, the use of RADIANCE for irradiance and illuminance calculations in an urban area is rather new. To obtain a representative picture of the solar and daylight availability on the studied site, RADIANCE computes a series of irradiance Gi (in [Wm-2]) and illuminance Ei (in [lx]) values by positioning virtual pyranometers and photometers at points located in front of all building external surfaces. Typically these measurements points are positioned on a regular grid, which width is comprised between 0.5 and 1 [m]. This grid of measurements is also prepared by PPF. Depending of which sky model is used, the computed values are representative of monthly or annual averages. If required the direct and diffuse components can be calculated separately.

Usually the calculations take into account two successive reflections (Figure 9). Thus the fraction of

light reaching a point after double reflections on adjacent buildings facades and on the ground is correctly modeled. The number of reflections can be changed for specific needs. For instance sky view factors can be obtained by computing irradiance values without reflections under a uniform sky (see section 3.2.4).

Skyvault

“virtual”pyranometeror luxmeter

Sky componentReflected component (1st order & 2nd order)

Figure 9: Three types of rays traced by RADIANCE to compute irradiance or illuminance values.

In order to automate all RADIANCE calculations performed on the models and to ensure results are

always produced using the same calculation parameters, automated procedures have been developed and grouped into the PPFCALC component. These procedures are defined in a UNIX Makefile [Tal89] that controls all simulations which involves many different steps. Thus, even users with no special knowledge of RADIANCE can start calculations. Moreover, PPFCALC also formats output data as pictures, graphs and tables.

3.2.4 Output data

RADIANCE simulations performed by PPFCALC result in large data sets containing, for each sample location i, the values si (sample area), Gi (irradiance), and Ei (illuminance). These values form the basic material that can then be analysed in several ways. This section presents the various methods


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that were devised for this purpose during the project. The Perolles area case study site serves here as an example.

Irradiation and illuminance distributions The large amount of irradiance Gi and illuminance Ei samples can be aggregated in order to

provide an overall view of the solar and daylight availability. For this purpose, histograms are produced with their horizontal axis subdivided into equal intervals of either irradiance or illuminance values. For each interval k, the total building external surface Sk that is lit by the corresponding level is calculated as:

)or( iiki

ik EGfsS ∑= in [m2]

where si is the area of the sample location i and fk a selection function defined by:

otherwise 0 k; intervalin falls if1)( xxfk =

Facade areas and roof areas can be separated at this stage. As an example, Figures 10 and 11 show such histograms computed for the Perolles area under different average sky models. Irradiances are reported as integrated values (irradiation) over the entire year and illuminance as average values for the total working year (8h to 18h). This choice appears more convenient since these units are probably better known by designers ([kWh] instead of [W] and [lx] instead of [lx h]). If required, integrated values can be converted to average values or vice-versa by a single multiplication factor. Such histograms give in fact a representation of the “solar availability” and “daylight availability” (see Annex 8.1) of an urban site. The histograms computed for all case study sites are presented in Annex 8.4.

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Irradiation distribution for Perolles site (orientation=0 sky=F00)

Roofs 38689 [MWh]Facades 32278 [MWh]

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Irradiation distribution for Perolles site (orientation=0 sky=FHH)

Roofs 15332 [MWh]Facades 13955 [MWh]

Figure 10: Global solar irradiation distributions for the Perolles area. Left: annual average sky model as illustrated in figure 5 (lower left corner). Right: heating season average sky model as illustrated in figure 5 (upper-left corner).

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Illuminance distribution for Perolles site (orientation=0 sky=F00)

Roofs 1073 [Mlm]Facades 864 [Mlm]


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Figure 11: Annual mean illuminance distribution for the Perolles area. Sky model as illustrated in figure 6.

These overall representations of solar and daylight availability can then serve to assess the potential for various renewable energy applications. In this project three applications have been investigated: passive solar techniques for heating, building integrated photovoltaic installations and daylighting techniques.

The focus has been set on vertical facades instead of roofs. The reason is simple: since all models

were built from 2D plans obtained from land use registers, they were simply extruded to their top height (i.e. with flat roofs). Thus, the true roof geometry is modeled with little accuracy. However, roofs are generally located at a height that does not largely vary between neighbouring buildings. This means they have a good view to the sky vault and shadowing effects are limited. The potential for solar energy collection on roof areas then remains nearly unaffected. On the contrary, neighbouring buildings largely affect facades. Hence their use as solar and daylight collectors requires some careful examination.

Qf : facades annual irradiation per square meter floor area To assess how much solar energy is falling on facades during a full year, the facades annual

irradiation per square meter floor area Qf can be calculated as:

area)floor (Gross1000 ⋅

⋅∆=

∑∈ facadesi

i

f

GtQ in [kWh m-2]

where ∆t is the annual diurnal time expressed in hours. Since Qf is given per unit floor area, it can be directly compared to building energy consumption indices.

Ppa : potential for passive solar heating techniques To quantify the potential for passive solar heating techniques, a global solar irradiation threshold is

first defined by calculating the irradiation Gpa_threshold required to equate the energy balance between solar gains and heat losses through a glazing during an entire heating season. Gpa_threshold depends on climatic and glazing characteristics: DD (degree-days for the heating season) in [Kd], thermal transmittance (i.e. U-value) in [Wm-2K-1] and total solar energy transmittance (i.e. g-value). It is given by the relation:

η⋅⋅⋅⋅

=gUDDG thresholdpa 1000

24_ in [kWh m-2]

where η is an utilisation factor reducing the total seasonal solar gains to account for the dynamic behaviour of the building and its occupants. The only essential component for accepting solar energy in a building is a glazed opening characterised by U and g values. The amount of useful heat depends on the passive solar technique that is used (e.g. windows, double-skin wall, translucent insulation, trombe wall). For the purpose of this project, no particular assumption about the applied technique was made. Therefore η has been set to 1, which appears as a rather optimistic assumption. Gpa_threshold values have been calculated with modern common glazing characteristics: U=1.3 [Wm-2K-1] and g=0.75 [-] (see Annex 8.2).


16

Finally the potential for passive solar heating techniques Ppa can be characterised by computing the

ratio of facade area receiving irradiation levels Gi above Gpa_threshold. Of course, these Gi values must be calculated using a radiometric average sky model restricted to the heating season (Figure 5).

∑∑

∈

∈

−⋅⋅=

facadesii

facadesithresholdpaii

pa s

GGzsP

)(100

_

in (%)

with z(x) the step function: otherwise 0 ;0 if 1)( ≥= xxz Ppv : potential for building integrated photovoltaic systems For PV systems installed on building facades, an annual irradiation Gpv_threshold=800 [kWh m-2] is

considered as a reasonable threshold [Gut99]. For roofs that have usually better exposures, a higher value should be assumed: 1000 [kWh m-2]. With a similar approach as for passive solar applications, irradiation values Gi computed under an annual radiometric average sky model (Figure 5) are then compared against Gpv_threshold in order to compute a ratio characterising the potential for building integrated PV systems Ppv :

∑∑

∈

∈

−⋅⋅=

facadesii

facadesithresholdpvii

pv s

GGzsP

)(100

_

in (%)

Pdl : potential for daylighting applications The same kind of procedure is applied to characterise the potential for daylighting applications. In

order to define an illuminance threshold Ethreshold without having to specify constructive details (e.g. glazing ratio, glazing luminous transmittance, room sizes, indoor surface reflectance, photometric properties of daylighting systems), a simple relation is assumed between the mean workplane illuminance Ew and the mean outdoor vertical illuminance on buildings envelopes Eo:

ow ECUE ⋅= in [lx]

where CU is a coefficient of utilisation [-] that accounts for the effects of all construction parameters mentioned above. Such kinds of coefficients have been calculated for several daylighting systems (e.g. in [Tre95]). For vertical openings, the order of magnitude of these coefficients usually lies around CU=0.05. If the workplane mean illuminance is fixed at Ew=500 [lx], the illuminance threshold value can be estimated for buildings facades as:

10000==CUE

E wthreshold in [lx]


17

The ratio Pdl is then obtained by comparing the illuminance values Ei calculated under a photometric average annual sky model (Figure 6) against Ethreshold :

∑∑

∈

∈

−⋅⋅=

facadesii

facadesithresholdii

dl s

EEzsP

)(100 in (%)

A similar threshold could also be calculated for roofs. Since skylight openings have usually slightly higher CU than vertical openings, the corresponding threshold value would be lower than the value previously computed for facades.

Obstruction Illuminance Multiplier An alternative method to assess the effect of urban form on daylight access can be devised: for

each point of the grid over building envelope, a ratio is computed between the illuminance Ei it receives and the illuminance it could have received without any obstruction. This ratio named “Obstruction Illuminance Multiplier (OIM)” has been recently proposed for North-South pairs of facades [Tsa99]. Here it has been computed for all surface orientations.

0

5000

10000

15000

20000

25000

0 0.2 0.4 0.6 0.8 1 1.2

Bui

ldin

g en

velo

pe a

rea

[m2]

Obstruction Illuminance Multiplier [-]

Obstruction Illuminance Multiplier distribution for Perolles site

RoofsFacades

0

5000

10000

15000

20000

25000

0 0.2 0.4 0.6 0.8 1 1.2

Bui

ldin

g en

velo

pe a

rea

[m2]

Obstruction Illuminance Multiplier [-]

Obstruction Illuminance Multiplier distribution for Perolles site

RoofsFacades

Figure 12: Obstruction Illuminance Multiplier [Tsa99] distributions for the Perolles area. Left: during the entire year. Right: during the heating season only.

Figure 12 show OIM distributions computed for the Perolles area. All surfaces that have OIM lower

than 1 are affected by obstructions. When the altitude of the sun is low (winter season) and with high surface reflectance, OIM can theoretically increase beyond 1 for certain facades. This indicates that their surrounding obstructions act as effective reflectors. For the Perolles area, this has not been observed on simulations made on the heating season. However, as shown on figure 12, 2265 [m2] of facades have been recorded with OIM=1 during the heating season compared to 1390 [m2] for the annual calculation. This is also an indication of a positive effect due to reflections over surrounding buildings.

Climatically independent characteristics All results presented so far are tightly dependent on the climatic characteristics of the site under

investigation through the use of the sky models. If climatic data are unavailable or if the effects of the urban form need to be examined irrespective of any specific geographical context, some other


18

parameters can be assessed using the same kind of procedure. First, outdoor daylight factors Di can be computed for every point by using a CIE standard overcast sky model:

h

ii E

ED ⋅= 100 in (%)

where Ei is the outdoor illuminance on building envelopes (measured vertically on facades) and Eh is the horizontal unobstructed illuminance. The corresponding histogram is illustrated on figure 13.

0

5000

10000

15000

20000

25000

30000

0 20 40 60 80 100 120

Bui

ldin

g en

velo

pe a

rea

[m2]

Daylight factor (%)

Daylight factor distribution for Perolles site

RoofsFacades

0

5000

10000

15000

20000

25000

0 0.2 0.4 0.6 0.8 1

Bui

ldin

g en

velo

pe a

rea

[m2]

Sky view factor [-]

Sky view factor distribution for Perolles site

RoofsFacades

Figure 13: Outdoor daylight factor (left) and sky view factor distributions (right) for the Perolles area.

Unlike previous studies, which were using the sky component as a single criterion [Lit92], the

daylight factors computed by RADIANCE also accounts for interreflections between buildings. By assuming an indoor target daylight factor of Dtarget=2% and the same coefficient of utilisation as before (CU=0.05), a threshold value Dthreshold can be computed as:

%CUD

D ettthreshold 40arg ==

Using this threshold, a new potential for daylighting application Pd can be computed as:

∑∑

∈

∈

−⋅⋅=

facadesii

facadesithresholdii

d s

DDzsP

)(100 in (%)

Sky view factors Sky view factors (SVF) can also be computed by using a uniform sky and ignoring the contribution

of interreflections:

h

ii E

ESVF = [-]

Although a sky view factor distribution gives similar indications as the outdoor daylight factors distribution (Figure 13), its calculation is much quicker to perform. Therefore SVF distributions can serve effectively as the first stage of a study to assist the planning process. Moreover SVF can be used to


19

assess the free view of the sky from windows which has a great impact on occupants’ satisfaction. SVF also directly affects the radiative cooling processes of an urban area [Oke88] [Eli91].

Both outdoor daylight factors and sky view factors do not vary if the site orientation is changed.

Thus they are solely indicative of building form and layout.

Orientation roses Although the orientation of an urban area is a concept that is often discussed, its definition does not

seem to be clear and unique. For instance orientation can be associated with the axis of the main roads or with the predominant orientation of buildings facades [Bar45].

For solar and daylight access analysis, the simplest method would be to calculate the facade

surface area oriented towards each direction. Then these areas can be summed and aggregated into several azimuth sectors (typically 15° wide). The sectors that contain the largest facade area indicate the potential orientations where the highest irradiation and illuminance levels could be coming from. However this method ignores the fact that some facades may be highly obstructed and therefore fail to perform as potential collectors. A simple alternative method, which deals more accurately with this issue, consists of applying weighting factors based on SVF when counting facade areas for every azimuth sector. Thus for each azimuth sector j, the total weighted facade area Aj is computed as:

∑∈

⋅=

jtortowardsorientedfacadesi

iij

SVFsA

sec5.0

in [m2]

The sum is performed over all measurements points attached to vertical facades oriented towards sector j. The 0.5 constant is the SVF of a point that belongs to an unobstructed vertical facade. This factor appears in the formula to ensure vertical unobstructed facades are fully counted in the sum. Figure 14 shows the orientation rose obtained by applying this method on the Perolles area. Orientation roses of all case study sites are shown in section 4.1.

E

N

W

S

-0 2000 4000 6000

Figure 14: Orientation rose for the Perolles area showing SVF-weighted facade areas (in [m2]) oriented towards each azimuth sector (15° wide).

The cross-like rose is due to the mostly rectangular shape of buildings and their regular arrangement on the site. This rose also indicates that the site is slightly more influenced by the South-West/North-East pair of azimuths. For an urban area mainly occupied by long building rows, the orientation rose would be clearly dominated by a single pair of directions.


20

The purpose of these orientation roses is also to serve at the early stage of urban planning before

solar and daylight availability distributions are computed. It is also expected that after some experience is gained by applying these procedures on several case study sites, some empirical relations could be found between the orientation rose and the solar and daylight availability.

Visualisations

Apart from site pictures as shown on figure 8, RADIANCE can be used to produce several types of visualisations. Figure 15 show two examples where the use of a false-colour scale helps the interpretation of the pictures physical contents. Similar picture analysis methods are also taken by other tools in the field [Mar00], [Rat00].

Figure 15: Visualisations of the Perolles site. Left: annual irradiation is coloured in red if Gi>Gpv_threshold and blue if Gi< Gpv_threshold. This enables a quick location of facade areas suitable for PV systems. Right: summer season direct horizontal irradiance at street level is coloured in red if it exceeds 50% of the direct unobstructed horizontal irradiance and in blue if it is below this level. This type of visualisation can for instance serve to assess outdoor summer comfort conditions or to locate appropriate zones for planting trees with special light requirements. Triangular shapes that appear particularly on the top right corner are due to the polygonal ground relief model. 3.3 Tool application on case study sites

This section briefly presents each site on which simulations have been performed. The Perolles case study site is presented with more details. All cases have been modelled within PPF from various data sources. Plans and view pictures of these models are presented along with simulation results in section 4.1. Climatic data that were used for the simulations are summarised in Annex 8.2.


21

3.3.1 Generic case study sites

6 hypothetical generic sites defined in a previous project [Ste97] have been studied. The 6 planning proposals for the same site have constant gross floor surface area and passive zone surface area. The concept of passive zone [Bak96] (which indicates the gross surface floor area located between 0 and 6[m] from the facade and thus benefiting from solar gains, daylighting and adequate natural ventilation) expresses a morphological parameter that has a determining influence on building energy consumption.

3.3.2 Existing case study sites

Athens case study sites

Simulations were performed for two large areas of the city: the Plaka area located near the historic centre and Pathsia, a more recent area. The corresponding PPF models were prepared from DXF files generated from aerial photographs.

These two sites contain thousands of building parts. Therefore, the RADIANCE simulations had to

be performed with interreflections bounded to the first order only in order to keep CPU time within reasonable limits.

Grugliasco case study sites

Two sites were chosen by the Grugliasco municipality (partner of the PRECis project): the Parad area that comprises large multi-storey residential blocks and the Borgo area formed by the historical centre of the city. In addition, two development plans were tested in the southern part of the Borgo area. PPF models were prepared from DXF files provided by Grugliasco municipality.

Perolles case study site

Fribourg is a small city (around 33'000 inhabitants) located around the “Sarine” river canyon since the 12th century. The lower and older part of the city lies at an altitude of 540 [m] while the surrounding plateau and hills, where the city has now extended, reach 690 [m]. Fribourg location and climate are similar to those of Bern, the Swiss capital city that is located 30 [km] away. During the 80's the geographical institute of the Fribourg University has conducted many detailed climatic surveys [Rot84] [Ruf86].

Recently, Fribourg municipality has joined a national action plan called "Cité de l'Energie" which

promotes sustainable energy policy in cities. Being interested by the PRECis project from its beginning, Fribourg municipality has suggested to choose a subpart of the Perolles area as a case study site and provided the relevant data from its land use register.

The Perolles area is located on a plateau between the railway line and a principal road (Boulevard de Perolles). It has been first built at the end of the 19th century when the Fribourg city expanded from


22

its ancient boundaries. At that time, deep valleys that ended in the Sarine canyon crossed the site. They have been filled to built the plateau.

Figure 16: The Perolles area in Fribourg (Switzerland): map and aerial view. The case study site is coloured in red on the map. The western part (shaped like a banana) which served for testing several hypothetical urban forms is coloured in yellow.

The case study site is an approximately square part of the Perolles area (Figure 16) occupied by a mix of light industrial buildings, warehouses, residential and commercial buildings. New residential/commercial buildings currently under construction in the central part of the site around a public green space. The western part of the site is presently occupied by old warehouses and can be considered as a fallow piece of land. In the future the municipality wants this area to be completely rebuilt. Fribourg municipality has recently ordered a masterplan study for this area and its surroundings. Since there are no definite plans yet available, this western part has been used to check several hypothetical urban forms. For these studies, the plot ratio was deliberately set to a relatively high value (2.0).

100 facades reflectances have been measured using a luminancemeter with a reference white

sheet positioned in front of facade samples according to a method presented in [Fon99]. Figure 17 shows the resulting distribution of reflectance values.


23

Figure 17: Facades reflectances measured in the Perolles area. Surprisingly the distribution spans over a rather large interval. The mean reflectance value is 0.35. Note that these are photometric measurements. Assuming “grey bodies”, these values could also serve for the calculation of solar irradiance within the urban texture.

West Cambridge case study site Two proposals for the extension of the University in the western part of the city were studied.

Having the same building layout, these proposals differ only by their number of storeys. The corresponding PPF models were entirely built from DXF files.

Trondheim case study site Two proposals for an area located around the hospital have been studied. Building shapes as well

as their layouts largely differ between the two cases.

3.4 Parametric simulations

The urban canyon has been adopted as the basic urban structural unit by many researchers [Oke88], [Arn90], [Swa93]. In order to follow a similar approach, a set of parametric simulations have been conducted over urban canyons with Height/Width ratios varying from 1/4 to 4/1. Canyons axis were also rotated in every directions with 15° steps. Simulation results were then formatted as polar diagrams presented in section 4.

A second set of parametric simulations were conducted to study the effect of small parks with 9[m]

high trees located in a dense building grid in Athens summer climate. Simulations results were provided by average irradiance ratios (=received irradiance over horizontal unobstructed irradiance) for every facades and for ground level also. They were then used to estimate the heat released to air by building envelopes and how streets air temperatures are affected [Dim00]. This last step was performed using a CFD program (see results given in CRES final report).


24

4. Results

4.1 Presentation of results

All results are summarised in the following tables that list Qf, Ppa, Ppv, Pdl and Pd. For the existing case study sites, the corresponding irradiation, illuminance and daylight factor distributions are given as histograms in annex 8.4. For each site and each parameter, the largest value is highlighted with bold font. The figures given in the tables are based on analysis performed only on buildings within the red dotted outlines shown on the plans. Orientation roses are presented with areas reported on relative scales. This means they should only be used to compare their shapes.

Results for the urban canyon parametric case study are given as polar diagrams sorted according

to canyon height/width (H/W) ratios (section 4.1.8). A ∅ sign replacing a polar diagram indicates a null potential in every direction. Figure 18 explains how to read these diagrams.

20 40 60 80 100 E

N

S

W

Canyon axis

A

B

AB

Figure 18: An example showing how to read polar diagrams presented in section 4.1.8. Once the canyon axis is set, a perpendicular axis is drawn and its intersections with the red curve show the potentials for both facades (A and B) facing these directions (here: 70% for facade A and 40% for facade B).

25

4.1.1 Generic case study sites

Urban area View Plan Orientation rose Climate Facades annual irradiation per

square meter floor area

% of facade area with irradiation ¥ Gpa_threshold during heating season

% of facade area with annual irradiation

¥800 [kWh m-2]

% of facade area with mean illuminance ¥ 10 [klx]

% of facade area with

daylight factor ¥ 40 %

Athens 270 [kWh m-2] 62 % 28 % 76 % Torino 239 [kWh m-2] 66 % 15 % 70 %

Fribourg 210 [kWh m-2] 66 % 3 % 68 % Cambridge 187 [kWh m-2] 68 % 2 % 65 %

Court Plot ratio = 1.7

Court 100 [m]

E

N

W

S Trondheim 195 [kWh m-2] 63 % 7 % 65 %

42 %



Pavilion-Court Plot ratio = 1.7

Pavilion-Court 100 [m]

E

N

W

S Trondheim 199 [kWh m-2] 63 % 7 % 66 %

59 %



Pavilion Plot ratio = 1.7

Pavilion 100 [m]

E

N

W

S Trondheim 282 [kWh m-2] 66 % 6 % 67 %

41 %



Slab Plot ratio = 1.7

Slab 100 [m]

E

N

W

S Trondheim 244 [kWh m-2] 59 % 9 % 59 %

55 %



Terrace-Court Plot ratio = 1.7

Terrace-Court 100 [m]

E

N

W

S Trondheim 204 [kWh m-2] 53 % 10 % 55 %

53 %

26



Terrace Plot ratio = 1.7

Terrace 100 [m]

E

N

W

S Trondheim 210 [kWh m-2] 50 % 14 % 50 %

67 %

4.1.2 Athens case study sites

Urban area View Plan Orientation rose Facades annual irradiation per


% of facade area with irradiation ¥ 76 [kWh m-2] during heating

season


¥800 [kWh m-2]




Plaka Plot ratio = 1.58

Plaka 100 [m]

E

N

W

S

353 [kWh m-2] 30 % 13 % 50 % 22 %

Pathsia Plot ratio = 2.88

Pathsia 100 [m]

E

N

W

S

255 [kWh m-2] 27 % 9 % 46 % 34 %

27

4.1.3 Grugliasco case study sites



% of facade area with irradiation ¥113 [kWh m-2] during heating

season


¥800 [kWh m-2]




Parad Plot ratio = 0.34

Parad 100 [m]

E

N

W

S

447 [kWh m-2] 78 % 21 % 81 % 77 %

Borgo present site Plot ratio = 0.25

Borgo 100 [m]

E

N

W

S

506 [kWh m-2] 72 % 22 % 76 % 71 %

Borgo proposal 1 Plot ratio = 0.43

Borgo_new_proposal_1 100 [m]

E

N

W

S

262 [kWh m-2] 75 % 39 % 84 % 89 %

Borgo proposal 2 Plot ratio = 0.35

Borgo_new_proposal_2 100 [m]

E

N

W

S

384 [kWh m-2] 71 % 42 % 84 % 91 %

28

4.1.4 Perolles case study site




season


¥800 [kWh m-2]




Whole area (present situation): plot ratio = 1.2

Perolles 100 [m]

E

N

W

S

224 [kWh m-2] 52 % 6.5 % 54 % 40 %

Stripes 1 (10 storeys) plot ratio = 2.0

STRIPES-01 100 [m]

E

N

W

S

309 [kWh m-2] 74 % 21 % 82 % 87 %


STRIPES-02 100 [m]

E

N

W

S

305 [kWh m-2] 82 % 35 % 97 % 95 %


STRIPES-03 100 [m]

E

N

W

S

315 [kWh m-2] 96 % 29 % 95 % 94 %

Towers 1 (15 storeys) plot ratio = 2.0

TOWER-01 100 [m]

E

N

W

S

286 [kWh m-2] 88 % 7 % 88 % 94 %

29


TOWER-02 100 [m]

E

N

W

S

260 [kWh m-2] 62 % 19 % 62 % 82 %


TOWER-03 100 [m]

E

N

W

S

289 [kWh m-2] 95 % 12 % 95 % 96 %


TOWER-04 100 [m]

E

N

W

S

274 [kWh m-2] 100 % 21 % 100 % 96 %

Comb 1 (6 storeys) plot ratio = 2.0

COMB-01 100 [m]

E

N

W

S

251 [kWh m-2] 67 % 21 % 68 % 67 %


COMB-02 100 [m]

E

N

W

S

255 [kWh m-2] 84 % 10 % 83 % 75 %


COMB-03 100 [m]

E

N

W

S

240 [kWh m-2] 70 % 20 % 73 % 71 %

30

Courtyard 1 (6 storeys) plot ratio = 2.0

COURTYARD-01 100 [m]

E

N

W

S

215 [kWh m-2] 69 % 16 % 69 % 55 %

Courtyard 2 (4 & 8 storeys) plot ratio = 2.0

COURTYARD-02 100 [m]

E

N

W

S

256 [kWh m-2] 80 % 15 % 82 % 71 %

Stepped slab blocks (5 and 8 storeys) plot ratio = 2.0

STEPPED_SLAB_BLOCKS 100 [m]

E

N

W

S

290 [kWh m-2] 81 % 17 % 83 % 79 %

31

4.1.5 Sensitivity to rotation of the Pérolles site




season


¥800 [kWh m-2]




15° rotation

E

N

W

S

224 [kWh m-2] 49 % 9.8 % 53 % 40 %

Whole area (present situation): plot ratio = 1.2

Perolles 100 [m]

E

N

W

S

224 [kWh m-2] 52 % 6.5 % 54 % 40 %

-15° rotation

E

N

W

S

224 [kWh m-2] 53 % 5.7 % 56 % 40 %

Irradiation visualisation (see figure 15 left)

for the +15° rotated Perolles area Irradiation visualisation (see figure 15 left)

for the existing Perolles area Irradiation visualisation (see figure 15 left)

for the -15° rotated Perolles area

32

4.1.6 West-Cambridge case study site




season


¥800 [kWh m-2]




Existing site Plot ratio = 0.1

West_Cambridge_op#1 100 [m]

E

N

W

S

309 [kWh m-2] 78 % 12 % 69 % 80 %

Proposal Plot ratio = 0.4


E

N

W

S

246 [kWh m-2] 77 % 11 % 66 % 77 %

Same proposal with 1 additional storey Plot ratio = 0.5


E

N

W

S

235 [kWh m-2] 74 % 10 % 63 % 71 %

33

4.1.7 Trondheim case study site




season


¥800 [kWh m-2]




Trondheim-Rit

Trondheim-Rit 100 [m]

E

N

W

S

213 [kWh m-2] 65 % 13 % 64 % 78 %

Trondheim-Rit2000

Trondheim-Rit2000 100 [m]

E

N

W

S

132 [kWh m-2] 55 % 10 % 55 % 60 %


34

4.1.8 Urban canyon case study HW H/W=1/4

Climate % façade area with potential for

passive solar application % façade area with potential for PV

applications % façade area with potential for

daylighting applications Athens

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Torino

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Fribourg

-0 20 40 60 80 100 E

N

S

W

-0 20 40 60 80 100 E

N

S

W

-0 20 40 60 80 100 E

N

S

W

Cambridge

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Trondheim

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W


35

HW H/W= 1/2





20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Torino

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Fribourg

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Cambridge

20 40 60 80 100 E

N

S

W

∅ 20 40 60 80 100 E

N

S

W

Trondheim

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W


36

HW H/W= 1/1





20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Torino

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Fribourg

20 40 60 80 100 E

N

S

W

∅ 20 40 60 80 100 E

N

S

W

Cambridge

20 40 60 80 100 E

N

S

W

∅ 20 40 60 80 100 E

N

S

W


37

Trondheim

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

H

W H/W= 2/1

Climate % façade area with potential for passive solar application

% façade area with potential for PV applications

% façade area with potential for daylighting applications

Athens

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Torino

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Fribourg

20 40 60 80 100 E

N

S

W

∅ 20 40 60 80 100 E

N

S

W


38

Cambridge

20 40 60 80 100 E

N

S

W

∅ 20 40 60 80 100 E

N

S

W

Trondheim

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

H

W H/W= 4/1

Climate % façade area with potential for passive solar application

% façade area with potential for PV applications

% façade area with potential for daylighting applications

Athens

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W


39

Torino

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Fribourg

20 40 60 80 100 E

N

S

W

∅ 20 40 60 80 100 E

N

S

W

Cambridge

20 40 60 80 100 E

N

S

W

∅ 20 40 60 80 100 E

N

S

W

Trondheim

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W


40

4.2 Discussion

There are several questions that arose during the project. This section will address some of them using results obtained for the case study sites that were investigated. It also highlights points that will require further work. It is expected that new questions could appear after the outcomes of this study are presented to urban planners.

Are the potentials for solar energy collection on building facades significant in urban areas? By looking at Qf figures, it appears that the primary solar energy that reaches building well exceeds

the final energy consumed by modern buildings or prescribed by actual standards. For instance in Switzerland the MINERGIE label prescribes annual heating final energy indices of 45 [kWh m-2] for new residential buildings and 90 [kWh m-2] for renovated buildings [Crd98]. This label is becoming mandatory in some regions (e.g. Fribourg) for public or publicly funded projects. With such standards, the potential for solar energy collection appears to be really significant. As shown on figure 19, this conclusion remains valid for all climates that were studied.

0 200 400 600

Athens

Torino

Fribourg

Cambridge

Trondheim

Qf [kWh m-2]

Figure 19: Qf ranges observed in all case study sites investigated by this project. Qf lower limits seem to vary with latitudes. More specifically, figures 20 to 22 show that the potentials for passive solar heating, PV and

daylighting techniques are far from negligible.

0 20 40 60 80 100

Athens

Torino

Fribourg

Cambridge

Trondheim

Ppa (%)

Figure 20: Ppa ranges observed in all case study sites investigated by this project.


41

0 20 40 60 80 100

Athens

Torino

Fribourg

Cambridge

Trondheim

Ppv (%)

Figure 21: Ppv ranges observed in all case study sites investigated by this project.

0 20 40 60 80 100

Athens

Torino

Fribourg

Cambridge

Trondheim

Pdl (%)

Figure 22: Pdl ranges observed in all case study sites investigated by this project. Are the potentials Ppa, Ppv and Pdl defined in meaningful terms for urban planning purposes? The potentials are calculated as area ratios and not energy ratios. For designers it is probably more

relevant to optimise solar and daylight access relatively to the building envelope area that will be adequately lit. This also fits well with aesthetic issues that designers consider with great attention. The amount of primary energy that can finally be used in the buildings is left for a next step in the design process.

Nevertheless, if the design first objective is to optimise renewable energy collection, the

comparisons could well be achieved by computing energy figures obtained by summing irradiation values Gi over the corresponding facade or roof areas.

If greatest potentials are searched for, are tower buildings the recommended solution? It is not surprising that compared to lower buildings high towers as studied for the Perolles area

(see section 4.1.4) offer larger potentials as long as the plot ratio is not increased simultaneously. However it is encouraging to find that other urban layouts with lower buildings (e.g. combs or courtyards) also reach significant potentials.


42

Why do polar diagrams for urban canyons show large potentials Ppa for East and West oriented facades?

This is the result of computing Gpa_threshold with the utilisation factor fixed to η=1. This value is much

too high and should be carefully adjusted. Figure 23 shows the dramatic change of the polar diagram obtained with η=0.5. The potential for passive solar heating techniques simply vanishes in East and West directions.

20 40 60 80 100 E

N

S

W

20 40 60 80 100 E

N

S

W

Figure 23: Polar diagrams illustrating Ppa for an urban canyon with H/W=1/1 in the Fribourg climate. Left: with Gpa_threshold

calculated using η=1 as in section 4.1.8. Right: with Gpa_threshold calculated using η=0.5.

Could Pd replace Pdl for characterising the potential for daylighting techniques? As illustrated on figure 24, Pdl remains generally higher that Pd. This means that an analysis based

on Pd only could hide potentially very effective urban designs. The large variations of Pdl for a single Pd value clearly illustrates that the urban texture orientation is playing a role as important as the building layout within it. This is also confirmed by the sensitivity study reported in section 4.1.5. Furthermore this indicates that anisotropic sky models are absolutely necessary if accurate analysis is required.

0

20

40

60

80

100

0 20 40 60 80 100

% o

f fac

ade

area

with

E>

10[k

lx] (

Pdl

)

% of facade area with D>40% (Pd)

Climate: AthensTorino

FribourgCambridgeTrondheim

Figure 24: Comparison of Pd and Pdl observed in all case study sites investigated by this project.


43

5. Conclusions

The tool developed within this project allows accurate quantification of the solar and daylight availability in urban areas. Once the required sky and 3D geometrical models are defined, the calculations of various figures characterising the potentials for passive solar heating techniques, building integrated photovoltaic systems and daylighting applications are fully automated. A large field of applications is conceivable for this tool:

• it can be used for checking whether solar and daylight availability are really optimised when

these issues are supposed to have been considered as important criteria in the design of old cities [Bar45];

• it can serve to validate the use of existing manual or simplified computer methods to estimate the solar irradiation of buildings in urban contexts [Lit98];

• consequences of urban planning guidelines and regulations on solar and daylight availability can be assessed prior to application;

• impact of new urban developments on existing building or solar collectors can be studied; • local problems such as glare due to highly glazed facades can be addressed; • appropriate locations for solar collectors on specific buildings can be identified; • some outdoor climate characteristics (at street level) can be estimated either to improve solar

and daylight penetration or, conversely, to ensure sufficient shading; • the effect of tree planting on solar and daylight access or on outdoor climate can be estimated

[Dim00]; The results achieved in this project for several case study areas located in Europe have proven that

the tool is operational. In addition they have shown that the solar and daylight availability in dense urban areas is already significant even without special planning measures regarding this issue. When care is taken, solar and daylight availability can certainly be increased further even for denser urban areas.

In the future, extensive use of this tool is expected to provide more detailed knowledge about how

to plan effective solar cities. This is in phase with objectives of recent projects (e.g. the solar city project currently planned by the IEA).

6. References

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44

[Cap00] I.G. Capuleto, E. Shaviv, SustArc- a model for the design of urban fabric with solar right considerations, PLEA 2000 proceedings, 2-5 Jully 2000, Cambridge, UK, (2000)

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[Cla96] J.A. Clarke, J.W. Hand, J. Hensen, K. Johnsen, K. Wittchen, C. Madsen, R. Compagnon, Integrated Performance Appraisal of Daylight-Europe Case Study Buildings, 4th European Conference "Solar Energy in Architecture and Urban Planning", Proceedings, Berlin, Germany, (1996)

[Com93] R. Compagnon, J.-L. Scartezzini, B. Paule, Application of Nonimaging Optics to The Development of New Daylighting systems, ISES Solar World Congress, Proceedings, Budapest, Hungary, (1993)

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[Esc91] G. Escourrou, Le climat et la ville, Nathan, Paris, (1991) [Fon99] M. Fontoynont (editor), Daylight performance of buildings, James&James Science

Publishers (1999) [Gra73] E. Grandjean, A. Gilgen, Umwelthygiene in der Raumplanung, Ott Verlag, Thun, (1973) [Gut98] M. Gutschner, S. Nowak, “Approach to assess the solar-yield-differentiated photovoltaic area

potential in the building stock,” proceedings of the 2nd World Conference on Photovoltaic Solar Energy Conversion, Vienna, (1998)

[Gut99] M. Gutschner, personal communication, NET-Nowak & Technologie SA, (1999) [Hey29] W.D. Heydecker, E. P. Goodrich, Sunlight and Daylight for Urban Areas, in Neighbourhood

and community planning by C.A. Perry, Regional plan for New York and its environs, (1929) [Hob99] R. Hobday, The Healing Sun, Findhorn Press, Findhorn, Scotlland, (1999) [Kno81] R. Knowles, Sun rhythm form, MIT Press , Cambridge - Mass. & London, (1981)


45

[Kno00] R. Knowles, The solar envelope; Its meaning for urban growth and form, PLEA 2000 proceedings, 2-5 Jully 2000, Cambridge, UK, (2000)

[Kun95] S. Kunz, METEONORM Meteorologische Grundlagen für die Sonnenenergienutzung, CISBAT’95 proceedings, 12-13 October 1995, EPF Lausanne, Switzerland, (1995) see also: www.meteotest.ch

[Lar98] G. Ward Larson, R. Shakespeare, Rendering with Radiance: the art and science of lighting visualization, Morgan Kaufmann Publishers, San Francisco, (1998) see also: radsite.lbl.gov/radiance/

[Las90] T.J. Lassar, Let the Sun Shine In, Architecture, May 1990, pp. 102-159, (1990) [Lit92] P. Littlefair, Access to Daylight and Sunlight, The Planner, 78(12), pp. 9-10, (1992) [Lit97] P. Littlefair, et al. POLIS: Urban Planning Research Actions to Improve the Solar Access,

Passive Cooling and Microclimate, in North Sun '97. Espoo-Otaniemi, Finland, (1997) [Lit98] P. Littlefair, Passive solar urban design: ensuring the penetration of solar energy into the

city, Renewable end Sustainable Energy Reviews, 2(3), pp. 303-326, (1998) [Mar97] J. Mardaljevic, Validation of a Lighting Simulation Program: A Study Using Measured Sky

Brightness Distributions, Lux Europa 97 Conference Proceedings, (1997) [Mar00] J. Mardaljevic, M. Rylatt, An image-based analysis of solar radiation for urban settings,

PLEA 2000 proceedings, 2-5 Jully 2000, Cambridge, UK, (2000) [Moo81] J. A. Moore, Daylighting in Manhattan; How inventive zoning can give city-dwellers their fair

share of the sun, Solar Age, 6, pp. 32-36, (1981) [Mor98] N. Morel, D. Lymberis, LESO-SHADE & LESO-COMFORT: two computer design tools, 10.

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[Nie96] A. Niewienda, F.D. Heidt, SOMBRERO: a PC-tool to calculate shadows on arbitrarily oriented surfaces, Solar Energy, 58(4-6), (1996) see also: nesa1.uni-siegen.de/softlab/sombre_e.htm

[Oke88] T.R. Oke, Street Design and Urban Canopy Layer Climate, Energy and Buildings, 11, pp. 103-113, (1988)

[Per90] R. Perez, P. Ineichen, R. Seals, J. Michalsky, R. Stewart, Modelling Daylight Availability and Irradiance Components from Direct and Global Irradiance, Solar Energy, 44(5), pp. 271-289, (1990)

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[Rid90] L. Ridley, Site, Community, and Urban Planning, in Solar building architecture / ed. by Bruce Anderson, MIT Press, Cambridge (Massachusetts), (1990)

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47

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7. Acknowledgements

This contribution to the EU project "PRECis: Assessing the potential for renewable energy in cities" (contract JOR3-CT97-0192) has been funded by the Swiss Federal Office for Education and Science (contract OFES 97.0096).

The “Service de l’Edilité de la Ville de Fribourg” has provided the data required to build the model for the Perolles area. Architect Martin Eisenring has designed the various urban forms investigated for the western part of the site. Noam Berchier and Luc Tomasetti, while studying architecture at EIF provided assistance for the preparation of the Perolles area geometric model.

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