Climate-Based Daylight Analysis for Residential Buildings

8/3/2019 Climate-Based Daylight Analysis for Residential Buildings

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Climate-Based Daylight Analysis

for Residential Buildings

Impact of various window configurations, external

obstructions, orientations and location on useful

daylight illuminance

Dr. John MardaljevicInstitute of Energy and Sustainable Development

De Montfort UniversityThe Gateway, Leicester, LE1 9BH, UK

e-mail [email protected]: +44 (0) 116 257 7972

Executive Summary: The IESD were commissioned by VELUX to carry out a paramet-ric climate-based daylight analysis for two residential building types with various windowconfigurations and external obstructions. Each of the ten building configurations wasevaluated for all combinations of eight orientations and six climate zones. Thus therewere 480 sets of unique climate-based daylight simulations. The evaluation was foundedon the useful daylight illuminance (UDI) scheme which determines the occurrence of ab-solute levels of illumination within four ranges: less than 100 lux; 100 to 500 lux, 500 to

2,500 lux; and, over 2,500 lux. The limits of these ranges are founded on human factorsdata from occupant surveys. The key indicator for good daylighting is the degree ofoccurrence of illuminances in the range 500 to 2,500 lux (labelled the UDI-a metric) sincethis range: provides adequate illumination for the majority of tasks; is associated witha very low probability for the switching-on of electric lights; and, the higher values inthis range are now believed to have beneficial effects for both productivity and long-termhealth. This study has shown that the addition of skylights invariably improves the over-all daylighting performance of the space. For some designs, the addition of skylights ledto a typical increase in the occurrence of the key UDI-a metric from 12% to 45% of theoccupied period of the year (i.e. 08h00 to 20h00).


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Contents

1 Introduction 7

2 Daylight Prediction 7

2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Climate-based daylight modelling . . . . . . . . . . . . . . . . . . . . . . . 72.3 UDI: A climate-based daylight metric . . . . . . . . . . . . . . . . . . . . . 8

2.3.1 An overview of findings on occupant response to varying levels ofdaylight illumination . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.2 Useful daylight illuminance range limits . . . . . . . . . . . . . . . . 102.3.3 UDI and good daylighting . . . . . . . . . . . . . . . . . . . . . . 12

2.4 Simulation methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.5 The climate data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.6 The building models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.7 Parametric scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.8 Generation of basic illuminance data . . . . . . . . . . . . . . . . . . . . . 312.9 Standard daylight factor evaluation and comparison with the DC compu-

tation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3 Results: Graphical Data 323.1 Workplane plots: Distribution in UDI and cumulative illumination . . . . . 32

3.1.1 UDI plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.1.2 Cumulative annual plots . . . . . . . . . . . . . . . . . . . . . . . . 323.1.3 Cumulative monthly plots - total and direct sun . . . . . . . . . . . 333.1.4 A note on viewing the plots . . . . . . . . . . . . . . . . . . . . . . 33

3.2 Plots of UDI metrics based on mean and median values . . . . . . . . . . . 383.2.1 Polar plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383.2.2 Individual cases across all climate and orientations . . . . . . . . . 383.2.3 Change in UDI plots . . . . . . . . . . . . . . . . . . . . . . . . . . 393.2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.3 Overall graphical summary of change in UDI metrics . . . . . . . . . . . . 433.4 Daylight factor plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4 Results: Tabular Data 604.1 Group 1: UDI metrics (hrs) across all climates and orientations . . . . . . 60

4.2 Group 2: UDI metrics (% occ yr) across all climates and orientations . . . 614.3 Group 3: Change in UDI metrics (hrs) across all climates and orientations 614.4 Group 4: Change in UDI metrics (% occ yr) across all climates and orien-

tations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614.5 Summary: Salient features in the data . . . . . . . . . . . . . . . . . . . . 62

4.5.1 A1 u to A2 u (Workplane 1) . . . . . . . . . . . . . . . . . . . . . . 624.5.2 A2 u to A3 u (Workplane 1) . . . . . . . . . . . . . . . . . . . . . . 624.5.3 A1 u to A3 u (Workplane 1) . . . . . . . . . . . . . . . . . . . . . . 634.5.4 B1 u to B2 u (Workplane 1) . . . . . . . . . . . . . . . . . . . . . . 634.5.5 Obstructed cases (as above - workplane 1) . . . . . . . . . . . . . . 634.5.6 Change from unobstructed to obstructed (Workplane 1) . . . . . . . 644.5.7 A1 u to A2 u (Workplane 2) . . . . . . . . . . . . . . . . . . . . . . 644.5.8 A2 u to A3 u (Workplane 2) . . . . . . . . . . . . . . . . . . . . . . 65

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4.5.9 A1 u to A3 u (Workplane 2) . . . . . . . . . . . . . . . . . . . . . . 654.5.10 B1 u to B2 u (Workplane 2) . . . . . . . . . . . . . . . . . . . . . . 654.5.11 Remaining for workplane 2: Obstructed cases and changes from

unobstructed to obstructed . . . . . . . . . . . . . . . . . . . . . . . 65

5 Conclusion and Discussion 86

Supplementary Reports (SRs) 88

References 89

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List of Figures

1 Daylight coefficient 145 patch scheme . . . . . . . . . . . . . . . . . . . . . 142 Climate file illuminance data for Toronto, Canada (interpolated to 15 minute

time-step). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 Climate file illuminance data for Munich, Germany (interpolated to 15 minutetime-step). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Climate file illuminance data for Nice, France (interpolated to 15 minutetime-step). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5 Climate file illuminance data for Finningley, UK (interpolated to 15 minutetime-step). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

6 Climate file illuminance data for Los Angeles, USA (interpolated to 15 minutetime-step). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

7 Climate file illuminance data for Seattle, USA (interpolated to 15 minutetime-step). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

8 Workplane areas superposed with the various window configurations . . . . 249 Three views of building A1 without (A1 u) and with (A1 o) external tree

obstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2510 Three views of building A2 without (A2 u) and with (A2 o) external tree

obstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2611 Three views of building A3 without (A3 u) and with (A3 o) external tree

obstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2712 Three views of building B1 without (B1 u) and with (B1 o) external tree

obstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2813 Three views of building B2 without (B2 u) and with (B2 o) external tree

obstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2914 Parametric scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3015 Workplane UDI plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3416 Workplane cumulative annual illuminance plots . . . . . . . . . . . . . . . 3517 Workplane cumulative monthly illuminance plots . . . . . . . . . . . . . . 3618 Workplane cumulative direct sun illuminance plots . . . . . . . . . . . . . 3719 Polar plot showing sensitivity of UDI metrics to building orientation . . . . 4020 Individual cases across all climate and orientations . . . . . . . . . . . . . 4121 Difference in metrics resulting from a change of the building type . . . . . 4222 Change in UDI metrics - Workplane: wp1 Cases: unobstructed . . . . . . . 4423 Change in UDI metrics - Workplane: wp2 Cases: unobstructed . . . . . . . 4524 Change in UDI metrics - Workplane: wp1 Cases: obstructed . . . . . . . . 4625 Change in UDI metrics - Workplane: wp2 Cases: obstructed . . . . . . . . 4726 Daylight factor - building: A1 u . . . . . . . . . . . . . . . . . . . . . . . . 4927 Daylight factor - building: A2 u . . . . . . . . . . . . . . . . . . . . . . . . 5028 Daylight factor - building: A3 u . . . . . . . . . . . . . . . . . . . . . . . . 5129 Daylight factor - building: A1 o . . . . . . . . . . . . . . . . . . . . . . . . 5230 Daylight factor - building: A2 o . . . . . . . . . . . . . . . . . . . . . . . . 5331 Daylight factor - building: A3 o . . . . . . . . . . . . . . . . . . . . . . . . 5432 Daylight factor - building: B1 u . . . . . . . . . . . . . . . . . . . . . . . . 5533 Daylight factor - building: B2 u . . . . . . . . . . . . . . . . . . . . . . . . 56

34 Daylight factor - building: B1 o . . . . . . . . . . . . . . . . . . . . . . . . 5735 Daylight factor - building: B2 o . . . . . . . . . . . . . . . . . . . . . . . . 58

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36 Comparison of UDI-c with DF for case A2 u, orientation 135, climateFRA Nice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

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List of Tables

1 The six climate files used in the study . . . . . . . . . . . . . . . . . . . . . 162 UDI metrics (hrs) for mean values across wp1 - all climates and orientations 673 UDI metrics (hrs) for median values across wp1 - all climates and orientations 68

4 UDI metrics (hrs) for mean values across wp2 - all climates and orientations 695 UDI metrics (hrs) for median values across wp2 - all climates and orientations 706 UDI metrics (% occ yr) for wp1 - mean values - all climates and orientations 727 UDI metrics (% occ yr) for wp1 - median values - all climates and orientations 738 UDI metrics (% occ yr) for wp2 - mean values - all climates and orientations 749 UDI metrics (% occ yr) for wp2 - median values - all climates and orientations 7510 Change in UDI metrics (hrs) for mean values across wp1 - all climates and

orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7711 Change in UDI metrics (hrs) for median values across wp1 - all climates

and orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

12 Change in UDI metrics (hrs) for mean values across wp2 - all climates andorientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

13 Change in UDI metrics (hrs) for median values across wp2 - all climatesand orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

14 Change in UDI metrics (% occ yr) for mean values across wp1 - all climatesand orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

15 Change in UDI metrics (% occ yr) for median values across wp1 - allclimates and orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

16 Change in UDI metrics (% occ yr) for mean values across wp2 - all climatesand orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

17 Change in UDI metrics (% occ yr) for median values across wp2 - allclimates and orientations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

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1 Introduction

The IESD were commissioned by Velux to carry out a parametric climate-based daylightanalysis for two residential building types with various window configurations and externalobstructions. Each of the ten building configurations was evaluated for all combinations

of eight orientations and six climate zones. Thus there were 480 sets of unique climate-based daylight simulations. The evaluation was founded on the useful daylight illuminancescheme. The structure of the report is as follows. An overview of the daylight predictionmethodology, the formulation of the daylight metrics and the scope of the parametricstudy are given in Section 2. The results are presented in Sections 3 and 4. The formand interpretation of the graphical data are presented in Section 3 (the graphical datathemselves are in supplementary PDF files). The results as tabular data are given inSection 4. The report closes with a Conclusion and Discussion (Section 5).

2 Daylight Prediction2.1 Background

It is acknowledged that by maximising the use of natural lighting (daylight) a significantreduction in artificial lighting and thus primary energy consumption can be achieved [ 1].A good provision of daylight is now considered to be highly desirable in terms of buildingoccupants well-being and productivity [2]. The goal of making good use of daylightprovision however needs to be tempered by the need to prevent the undue occurrenceof very high levels of daylight illuminance since these are associated both with visualdiscomfort and the likelihood of excessive solar gain (i.e. increased cooling loads).

Design guidelines recommend daylight provision in terms of the long-established day-light factor (DF). Formulated in the UK over fifty years ago, the daylight factor is simplythe ratio of internal illuminance to unobstructed horizontal illuminance under standardCIE overcast sky conditions [3]. It is usually expressed as a percentage, so there is noconsideration of absolute values. The luminance of the CIE standard overcast sky is rota-tionally symmetrical about the vertical axis, i.e. about the zenith. And, of course, thereis no sun. Thus for a given building design, the predicted DF is insensitive to either thebuilding orientation (due to the symmetry of the sky) or the intended locale (since it issimply a ratio). In other words, the predicted DF value would be the same if the buildinghad North-facing window in Stornoway or South-facing window in Brighton. The same

would be true if the locations were Seattle and Miami - or indeed for any city in anycountry.The inability of the currently-used methods to quantify absolute measures of natural

illumination with any reliability has resulted in a situation where the promotion of daylightrests on vaguely substantiated claims regarding the actual benefits. A significant factorthat hinders the widespread uptake of daylighting systems is the lack of an evaluativescheme to determine their actual performance under realistic conditions.

2.2 Climate-based daylight modelling

It now appears to be widely accepted that the daylight factor method does not allowfor improvement by incremental means, and that significant advancement can only beachieved by considering predictions for absolute values of daylight illuminance founded

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on realistic meteorological data, i.e. climate-based daylight modelling. Climate-baseddaylight modelling is the prediction of various radiant or luminous quantities (e.g. ir-radiance, illuminance, radiance and luminance) using sun and sky conditions that arederived from standard meteorological datasets [4]. Climate-based modelling delivers pre-dictions of absolute quantities (e.g. illuminance) that are dependent both on the locale(i.e. geographically-specific climate data is used) and the building orientation (i.e. theillumination effect of the sun and non-overcast sky conditions are included), in additionto the buildings composition and configuration.

The term climate-based daylight modelling does not yet have a formally accepteddefinition - it was first coined by Mardaljevic in the title of a paper given at the 2006CIBSE National Conference [4]. However it is generally taken to mean any evaluationthat is founded on the totality (i.e. sun and sky components) of contiguous daylight dataappropriate to the locale for a period of a full year. In practice, this means sun andsky parameters found in, or derived from, the standard meteorological data files whichcontain hourly values for a full year. Given the self-evident nature of the seasonal pattern

in daylight availability, an evaluation period of a full year is needed to fully capture all ofthe naturally occurring variation in conditions that is represented in the climate dataset.The exact pattern of hourly values in a standard climate dataset is unique and, becauseof the random nature of weather, it will never be repeated in precisely that way. Climatedatasets are however representative of the prevailing conditions measured at the site, andthey do exhibit much of the full range in variation that typically occurs.

A climate-based analysis is intended to represent the prevailing conditions over aperiod of time, rather than be simply a snapshot of specific conditions at a particularinstant. Because of the seasonal variation of daylight, the evaluation period is normallytaken to be an entire year, although sometimes seasonal or monthly analyses may be

required. Analyses may be restricted to include just those hours in the year that cover,for example, the working period. There are a number of possible ways to use climate-based daylight modelling [5, 6, 7, 8, 9]. The two principal analysis methods are cumulativeand time-series.

A cumulative analysis is the prediction of some cumulative measure of daylight (e.g.total annual illuminance) founded on the aggregated luminance (or radiance) effect of(hourly) sky and the sun conditions derived from the climate datset. It is usually de-termined over a period of a full year. This could equally be carried out on seasonal ormonthly basis, i.e. predicting a cumulative measure for each season or month in turn.The cumulative method can be used for predicting the micro-climate and solar access inurban environments, the long-term exposure of art works to daylight, and the seasonaldynamics of daylight and/or shading at the early design stage.

Time-series analysis involves predicting instantaneous measures (e.g. illuminance)based on all the hourly (or sub-hourly) values in the annual climate dataset. These pre-dictions are used to evaluate, for example, the overall daylighting potential of the building,the occurrence of excessive illuminances or luminances, as inputs to behavioural modelsfor light switching and/or blinds usage, and in assessing the performance of daylightresponsive lighting controls.

2.3 UDI: A climate-based daylight metric

For this study, the evaluation is founded on an analysis of a time-series of predicteddaylight illuminance values across the workplane. Time-varying daylight illuminance val-

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ues were predicted at approximately 1000 points evenly distributed across the workplane.The analysis of the predicted illuminance values was carried out using the useful daylightilluminance (UDI) scheme [10, 11, 7, 4, 8].

2.3.1 An overview of findings on occupant response to varying levels of day-light illumination

It is acknowledged that there is a large range of lighting conditions over which the humaneye performs satisfactorily, and that there is a large range of variation among individualsas to what comprises satisfactory visual conditions. Whilst there are no absolutely con-clusive studies that correlate daylighting provision or occupant satisfaction with workerproductivity, there is mounting evidence that workers do appreciate offices that providedaylight and a view of the outside, and that glare-free and thermally comfortable spaceshave quantifiable effects on workers satisfaction and performance [12, 13].

The UK Chartered Institution of Building Services Engineers (CIBSE) Code for Inte-

rior Lighting recommends that offices should have a design illuminance level of 500 lux. Adesign illuminance of 500 lux is, in fact, commonplace throughout much of the developedworld. Consequently, electric lighting is usually designed to deliver 500 lux of (artificial)illuminance evenly across the workplane. When sufficient daylight is available, electriclighting may be reduced, or switched off altogether, by either the occupants themselvesor some control mechanism.

The Cost-Effective Open-Plan Environment (COPE) field study, conducted by theInstitute for Research Construction (National Research Council Canada) recorded thatilluminances larger than, or equal to, 150 lux were classified as appreciable daylight [14].Furthermore, the Illuminating Engineering Society (IES) of North America recommends50 to 100 lux, provided directly onto the individual task area, as the general range ofilluminance required for working with CRT screens in laboratory areas [15]. In fact, duringa survey of the work spaces of a computer hardware and software distribution company,where each of the offices contained at least two computers, measurements showed thatmost employees felt comfortable with a lighting level of around 100 lux (as opposed tothe standard regulations of workplaces demanding 300 lux to 500 lux at desk level) [16].It has also been observed that people tend to tolerate much lower illuminance levels ofdaylight than artificial light, particularly in diminishing daylight conditions at the end ofthe day, such as continuing to read at daylight levels as low as 50 lux [ 17].

In a field study carried out by Lawrence Berkeley National Laboratory (USA), officeworkers were allowed to create their own lighting environment by manually controlling

blade angles of mechanical Venetian blinds and varying the intensity of electric lighting.The illuminances recorded during the study were in the range 840 lux to 2146 lux in themorning and 782 lux to 1278 lux in the afternoon. This indicated that the occupantseither preferred or, at least, tolerated higher light levels than those set by the automaticcontrol system (510 lux to 700 lux) [12].

Studies relating to office workers impressions of daylight and lighting signified thatmost office occupants wanted to work under some form of daylighting. However, in heavilyglazed offices, people were often less satisfied due to the high levels of daylight provisionand the associated propensity for discomfort [18]. While noting that satisfaction withdaylight is a complicated issue depending on many other factors such as facade orientation,

obstructions, and the effectiveness of shading devices, levels of daylight that are consideredtoo low may easily be supplemented by electric lighting, whereas levels that are too highare associated with problems that are more complex to deal with (for example, glare and

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overheating) [18]. In fact, occupant surveys have uncovered shortcomings for conventionaldesign practice and have expanded the definition of an adequate office visual environment[19]. For example, variation in daylight levels is considered desirable provided that therange in experienced levels is not too great. Occupants prefer a space with a variation inthe natural light pattern, and where they have a slightly higher task illuminance than thegeneral surround illuminance, their visual perception can be enhanced [19]. Furthermore,researchers have noticed that lighting levels that are markedly higher than the typicaldesign workplane illuminance level (for example, 500 lux) are tolerated by the occupantsunless there is glare or direct sun, in which case the occupants may opt to operate ashading device [20]. Observations made by Roche over several weeks suggested that thevisual environment, when facing a computer workstation which was at a right angle to thewindow (as is recommended), was reasonably comfortable when the workplane illuminancewas below 1800 lux [18, 21]. During that same experiment, it was noted that the daylightilluminance range of 700 lux to 1800 lux appeared to be acceptable for both computerand paper-oriented tasks.

The visual effects of lighting have been an area of investigation since the emergenceof the science of optics hundreds of years ago. The physiological effects of illuminationlevels on humans, however, are a relatively recent area of study. Following the discoveryin 2002 of novel photoreceptor cells in the eye, with additional nerve connections to thebrain, it is now better understood how light influences and controls a large number ofbiochemical processes in the human body. Significant amongst these is the control of thebiological clock and the regulation of important hormones through consistent light-darkrhythms [22]. These studies have revealed that light has a significant influence on health,wellbeing, alertness, and even the quality of sleep that is much greater than was suspectedonly 25 years ago [23]. Additionally, several recent studies, such as that by Partonen [24],

show that bright light exposure improves mood and reduces depressive symptoms amongsubjects working indoors, particularly in winter for locales with high latitudes. In fact,more and more studies are bolstering the notion that current indoor lighting levels andstandards are too low for biological stimulation as well as for most peoples preferences,and that the criteria for good lighting need to be reconsidered [23, 25].

2.3.2 Useful daylight illuminance range limits

Put simply, achieved UDI is defined as the annual occurrence of illuminances acrossthe work plane that are within a range considered useful by occupants. The rangeconsidered useful is based on a survey of reports of occupant preferences and behaviour

in daylit offices with user operated shading devices. Daylight illuminances in the range 100to 500 lux are considered effective either as the sole source of illumination or in conjunctionwith artificial lighting. Daylight illuminances in the range 500 to around 2,000 or maybe2,500 lux are often perceived either as desirable or at least tolerable. Note that thesevalues are based on surveys carried out in non-residential, largely office buildings wheredaylight-originated glare on visual display devices is a common problem. Many of thesesurveys were carried out before LCD display panels - which are much less prone to glarethan CRT screens - became commonplace. In contrast to office buildings, tasks in thedomestic setting are not, of course, largely desk and display screen orientated. For thesereasons, it is believed reasonable to recommend a higher upper limit for UDI achieved for

the residential setting than for the office environment. Accordingly, the upper limit forpreferred/tolerated daylight illuminance used for this study was 2,500 lux.

UDI achieved therefore is the defined as the annual occurrence of daylight illuminances

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that are between 100 and 2500 lux. The UDI range is further subdivided into two rangescalled UDI-supplementary and UDI-autonomous. UDI-supplementary gives the occur-rence of daylight illuminances in the range 100 to 500 lux. For these levels of illuminance,additional artificial lighting may be needed to supplement the daylight for common taskssuch as reading. UDI-autonomus gives the occurrence of daylight illuminances in therange 500 to 2500 lux where additional artificial lighting will most likely not be needed.The UDI scheme is applied by determining at each calculation point the occurrence ofdaylight levels where:

The illuminance is less than 100 lux, i.e. UDI fell-short (or UDI-f).

The illuminance is greater than 100 lux and less than 500 lux, i.e. UDI supplemen-tary (or UDI-s).

The illuminance is greater than 500 lux and less than 2,500 lux, i.e. UDI autonomous(or UDI-a).

The illuminance is greater than 100 lux and less than 2,500 lux, i.e. UDI combined(or UDI-c).

The illuminance is greater than 2,500 lux, i.e. UDI exceeded (or UDI-e).

As noted, the UDI ranges were based on a distillation of values from surveys carried outin office spaces, and many of them before LCD screens became commonplace. Also, therecent findings regarding the role of illumination in maintaining the circadian rhythmsuggest that regular exposure to high illuminances during daytime could have long-termbeneficial health effects [26]. Webb notes a Japanese study by Noguchi who found that:

...bright lighting in the office (2500 lux compared to 750 lux, provided for2 hours in the morning and one hour after lunch for several weeks) boostedalertness and mood, especially in the afternoon. It also seemed to promotemelatonin secretion and fall in body temperature at night, changes that shouldimprove the quality of sleep. Although this work was based on a small numberof people and further work is needed, it shows promise for alterations in officelighting in terms of productivity and health of the workers.

Thus it is recommended here that the occurrence of illuminances greater than 2,500 lux(i.e. UDI-e) should not, by design, be eliminated altogether, and that moderate occurrence

may in fact be beneficial. What exactly the optimum levels of exposure might be is notyet known. For those cases where solar gain in summer must be controlled to minimisecooling requirements, careful attention should be paid to the degree of occurrence of theUDI-e metric.

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2.3.3 UDI and good daylighting

Whilst there are no official guidelines or recommendations yet for illuminance levels pre-dicted using climate-based modelling, there is sufficient evidence in the published litera-ture to propose the following:

Good daylighting is deemed to be that which offers high levels of useful day-light (i.e. 100 to 2,500 lux), and where a significant part of the occurrence ofuseful daylight is due to illuminances that fall within the autonomous range(i.e. 500 to 2,500 lux). Furthermore, recent findings regarding the beneficialhealth effects of occasional high illuminances (i.e. greater than 2,500 lux) sug-gest that moderate occurrences of UDI exceeded should de considered desirableand not excluded altogether.

Provision of adequate levels daylight illuminance is known to affect the use of electriclighting. For non-domestic buildings a number of studies have found that the switch-on

probability is small for desktop illuminances above 250 lux [27, 28]. At present, it isuncertain how these findings for users in office buildings might relate to user behaviourin a domestic setting - this is clearly an area where information is lacking at present.Nonetheless, it is reasonable to suppose that similar behaviour will ensue, and so goodlevels of daylight illuminances are likely to be associated with lower levels of electriclighting usage. Consequently, the following can be reasonably assumed or stated:

The switch-on probability will be high for illuminance less than 100 lux (i.e. UDI-e).

The switch-on probability will reduce from high to low as the illuminances increasefrom 100 to 500 lux (i.e. that covered by the UDI-s range).

There is significant variability and associated uncertainty in user switching be-haviour over the illuminance range where the probability of switching on reducesfrom high to low.

Thus, there is reasonable certainty that an illuminance in the UDI-a range (i.e. 500to 2,500 lux) will not result in a switch-on, whereas there is considerable uncertaintyregarding the probability of a swicth-on event when the illuminance is in the UDI-s range(i.e. 100 to 500 lux). Accordingly, maximization of the occurrence of the UDI-a metricshould be taken as the most reliable indicator that the overall level of electric lightingusage (for that space) will be low.

2.4 Simulation methodology

In principle, climate-based daylight modelling (CBDM) could be carried out using eithercomputer simulation techniques or scale models in a sky simulator (i.e. physical mod-elling). To date however CBDM has been demonstrated using only computer simulationtechniques.1 Computer simulation techniques were used for this study.

The basic steps in the evaluation were as follows - for every combination of buildingorientation and locale:

1Sky simulator domes are subject to both fundamental limiting factors such as parallax error [29]

and several practical/operational constraints such as lamp stability, incomplete sky coverage and thedemonstrated inaccuracy of scale model construction [30].

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1. Obtain basic climate data from the designated standard meteorological file for thatlocale.

2. Generate a sky luminance distribution using a sky model based on the value fordiffuse horizontal illuminance in the climate data.

3. Create a description of the sun (position and luminance) from the value of directnormal illuminance in the climate data.

4. Compute the internal daylight illuminance distribution (repeat steps 2 to 4 for eachtime-step).

5. Process the illuminance predictions to determine various daylight metrics and gen-erate plots etc.

A so called brute-force approach would require a full lighting simulation for each of

the four thousand or so unique sky and sun configurations that can be derived from all thedaylight hours in the climate dataset. Although a brute-force approach is tractable, thesimulation times may prove excessively long. In 1983, Tregenza and Waters described anaccelerated approach to predict internal illuminance based on what they called daylightcoefficients [31]. The daylight coefficient approach requires that the sky be broken intomany patches. The internal illuminance at a point that results from a patch of unit-luminance sky is computed and cached. This is done for each patch of sky. It is thenpossible, in principle, to determine the internal illuminance for an arbitrary sky luminancedistribution (and sun luminance/position) using relatively simple (i.e. quick) arithmeticoperations on matrices. The computational expense of a daylight coefficient calculation fora sky with N patches is comparable to that for N standard simulations. Provided therefore

that the number of patches is less than the number of skies that need to be modelled, thetechnique has the potential to be computationally more efficient than treating each skyindividually.

The computational engine that was used to predict the daylight daylight coefficientswas the freely available Radiance lighting simulation system [32]. The Radiance system isthe most rigourously validated lighting simulation program currently available. Radiancehas been proven to be capable of high accuracy predictions and it has become a de factostandard for researchers and practitioners world-wide.

The basic daylight coefficient (DC) scheme described by Tregenza and Waters wasimplemented into the Radiance lighting simulation system and illuminance tested against

the BRE-IDMP validation dataset [33]. That scheme divided the sky into 145 patchesand used these to determine the contribution from both the sun and the sky. The basicDC scheme produced large errors whenever there was a significant divergence between theactually occurring sun position and the nearest pre-computed DC patch value. The basicDC scheme was improved and a refined formulation was devised which gave accuraciescomparable the best that could achieved using the standard Radiance calculation method,i.e. typically within 10% of measurements from the BRE-IDMP validation dataset[5, 34].

The refined DC method computes separate coefficients for the direct and diffuse com-ponents from the 145 patches on the hemisphere. A schematic is shown in Figure 1.

Additionally, the direct sun component is determined from a finely discretised set of 5000patches on the hemisphere. This ensures that the important direct sun component is pre-cisely determined. This also allows a time-step shorter than one hour to be used without

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loss of precision. Preparatory tests showed that the hourly time-step of the climate dataneeds to be reduced using suitable interpolation methods to minimise alias-like samplingerrors in the prediction of the degree (magnitude and occurrence) and spatial distributionof direct solar illumination. The prediction of this component needs to be both reliableand consistent so as not to confound the metrics since they are particularly sensitive tothe degree and occurrence of direct solar illumination. Interpolation of the climate datato a 15 minute time-step was found to be sufficient to finely resolve the progression ofthe sun thereby reducing sampling errors to a minimum. Internal daylight lluminancestherefore were predicted at 15 minute intervals. The sky model mixing function describedin Mardaljevic [35] was used to determine the varying sky luminance patterns at eachtime-step. Eight building orientations were evaluated covering the 360 compass range insteps of 45.

Note that Radiance was used in its standard backwards ray-tracing mode. The sky-lights for building B had a moderately deep shaft, however the sides had a diffuse finishand the light transfer through the skylight shaft can be accurately modelling using stan-

dard backwards ray-tracing. The forward ray-tracing add-on Pmap would be required tomodel specular finish and/or very long shafts or light-pipes.

145 patches distributed across the hemisphere

Figure 1: Daylight coefficient 145 patch scheme

2.5 The climate data

The principal sources of basic data for climate-based daylight modelling are the standardclimate files which were originally created for use by dynamic thermal modelling programs[36]. These datasets contain averaged hourly values for a full year, i.e. 8,760 values for

each parameter. For lighting simulation, the required parameters may be either of thefollowing pairs:

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Global horizontal irradiance and either diffuse horizontal irradiance or direct normalirradiance.

Global horizontal illuminance and either diffuse horizontal illuminance or directnormal illuminance.

Standard climate data for a large number of locales across the world are freely availablefor download from several websites. One of the most comprehensive repositories is thatcompiled for use with the EnergyPlus thermal simulation program [37]. Climate datafor the six locales used in this study were sourced from the EnergyPlus website2. TheEnergyPlus weather data files are derived from 16 sources. They have all undergonevalidation procedures with varying degrees of rigour. Comprehensive documentation (e.g.definitions, format, etc.) is also available for download from the EnergyPlus website.

The six locales were Toronto (Canada), Munich (Germany), Nice (France), Finningley(UK), Los Angeles (USA) and Seattle (USA). The lat/lon coordinates of each city/stationand the short name ID given for this study are listed in Table 1. The climate file data usedfor the simulations was diffuse horizontal illuminance and direct normal illuminance. Thepattern of hourly values in a climate dataset is unique and, because of the random natureof weather, it will never be repeated in precisely that way. Climate datasets are howeverrepresentative of the prevailing conditions measured at the site, and they do exhibit muchof the full range in variation that typically occurs. Furthermore, these standard datasetsprovide definitive yardstick quantities for modelling purposes. The last column in Table 1gives the number of sunny for each of the climate files. There is no widely accepteddefinitive definition for the occurrence of a sunny day in a climate file. Here, a sunny daywas taken to be one where more than half of the daily total of global horizontal illuminancewas due to direct solar radiation. This quantity varied from 39 days (Finningley) to 221

(Los Angeles) and appears to serve as a sensitive discriminator to summarize the overalldegree of sunnyness for the climates.

The results for one locale will be applicable to nearby places with similar climates.For example, the Finningley (UK) dataset should be applicable to much of central andnorthern England. However, care should be taken in extrapolating results where it isknown that there are significant gradients in the prevailing meteorology over relativelyshort spatial scales, as can often be the case for coastal zones and those locales near to,say, mountain ranges. Further, more exhaustive, sensitivity testing would delineate thelimits of extrapolation of results from one locale to another.

The climate file illuminance data interpolated to a 15 minute time-step is shown in

Figures 2 to 7. Interpolated values of diffuse horizontal illuminance and direct normalilluminance over a period of a full year are shown as (tiny) shaded rectangles arranged ina 365 (i.e. days of the year) by 96 (i.e. 24 hours in 15 minute steps) matrix. The diffusehorizontal illuminance is the visible energy (i.e. light) from the sky that is incident on anunobstructed horizontal surface. The direct normal illuminance is the visible energy fromthe sun and circumsolar region incident on a surface that is normal to the direction ofthe sun. Local time is shown, i.e. summertime is local time plus one hour. The start andend period of summertime varies slightly from year to year and between locations. Forconsistency, the start and end dates were the same for each locale: day numbers 85 and301 respectively. These days are indicated by vertical dashed lines in each of the figures.

The hours of the day over which daylight availability was assessed was taken to be the2http://www.eere.energy.gov/buildings/energyplus/cfm/weather data.cfm

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http://www.eere.energy.gov/buildings/energyplus/cfm/weather_data.cfmhttp://www.eere.energy.gov/buildings/energyplus/cfm/weather_data.cfm


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period 08h00 tp 20h00. These times are indicated in the figures by horizontal dashedlines.

The shading in Figures 2 to 7 represents the magnitude of the illuminance with zerovalues shaded light-gray. Presented in this way it is easy to appreciate both the pre-vailing patterns in either quantity and their short-term variability. Most obvious is thedaily/seasonal pattern for both illuminances: short periods of daylight in the wintermonths, longer in summer. The hour-by-hour variation in the direct normal illuminance(smoothed by interpolation to a 15 minute step) is clearly visible, though it is also presentto a lesser degree in the diffuse horizontal illuminance (i.e. from the sky).

Of course, both diffuse and direct illuminances will, in reality, vary over periods shorterthan an hour. Interpolation of the dataset to a time-step shorter than one hour will pro-vide a smoother traversal of the sun, and so reduce the potential for alias-like artefacts inthe realisation of the directly illuminated surfaces in the simulation. Interpolation alonehowever will not introduce short-term variability into the values for diffuse horizontal anddirect normal illuminance. This variability would have to be synthesised using stochas-

tic models [38]. This however is only to be recommended for detailed investigation ofbehavioural models and so was not considered appropriate for this study.

ID City/ Country Latitude Longitude SunnyStation days

CAN Toro Toronto Canada 43.67 79.63 138DEU Muni Munich Germany 48.13 -11.70 78FRA Nice Nice France 43.65 -7.20 182GBR Finn Finningley UK 53.48 1.00 39USA LosA Los Angeles USA 33.93 118.40 221

USA Seat Seattle USA 47.45 122.30 109

Table 1: The six climate files used in the study

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Direct Normal Illuminance

1 2 3 4 5 6 7 8 9 10 11 12Month

0

4

8

12

16

20

24

Hour

Diffuse Horizontal Illuminance

1 2 3 4 5 6 7 8 9 10 11 12Month

0

4

8

12

16

20

24

Hour

lux

0

20000

40000

60000

80000

Figure 2: Climate file illuminance data for Toronto, Canada (interpolated to 15 minu

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1 2 3 4 5 6 7 8 9 10 11 12Month

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1 2 3 4 5 6 7 8 9 10 11 12Month

0

4

8

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lux

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20000

40000

60000

80000

Figure 3: Climate file illuminance data for Munich, Germany (interpolated to 15 min

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1 2 3 4 5 6 7 8 9 10 11 12Month

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Hour


1 2 3 4 5 6 7 8 9 10 11 12Month

0

4

8

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40000

60000

80000

Figure 4: Climate file illuminance data for Nice, France (interpolated to 15 minute

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1 2 3 4 5 6 7 8 9 10 11 12Month

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1 2 3 4 5 6 7 8 9 10 11 12Month

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60000

80000

Figure 5: Climate file illuminance data for Finningley, UK (interpolated to 15 minu

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1 2 3 4 5 6 7 8 9 10 11 12Month

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1 2 3 4 5 6 7 8 9 10 11 12Month

0

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60000

80000

Figure 6: Climate file illuminance data for Los Angeles, USA (interpolated to 15 min

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1 2 3 4 5 6 7 8 9 10 11 12Month

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Figure 7: Climate file illuminance data for Seattle, USA (interpolated to 15 minut

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2.6 The building models

The 3D building geometry for the simulations was provided by VELUX already in Ra-diance format. Daylight provision for a total of ten variants of two building types wasevaluated. Residential building type A had three window variants: A1, A2 and A3,

indicating no skylights, two skylights and two pairs of three skylights, respectively. Resi-dential building type B had two window variants: B1 and B2, indicating no skylights andfour skylights, respectively. Views from above of each of the five building types is shownin Figure 8 with superposed images of the workplane areas. For both building types, theillumination across two distinct workplane areas wp1 and wp2 was evaluated - these areshaded red and blue in Figure 8. Horizontal illumination at a height of 0.7 m above thefloor was predicted for the two workplane areas, though the workplanes themselves werenot modelled as actual objects. It should be noted that, for building B, workplane 2 hasno skylight above it for any of the situations.

Internal surfaces were assigned diffuse reflectance values typical of ceiling, walls and

floor, i.e. 0.70, 0.55 and 0.25 respectively. All windows were modelled as clear double-pane6mm low-emissivity with a transmittance of 0.74.Three views of each of the ten building configurations (i.e. five building types each

without and with obstructions) are given in Figures 9 to 13. The external obstructionswere ovoid shapes intended to represent trees. These were given a reflectance of 0.20 andshould be considered as providing only light to moderate obstruction. In addition to thetrees, there was a cylindrical wall which represented the obstruction to the horizon thatwould be expected in any typical housing development.

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A3_u

wp1

A2_u

wp1

wp2

A1_u

wp1

wp2

B1_u

wp1

wp2 B2_u

wp1

wp2

N

Figure 8: Workplane areas superposed with the various window configurat24

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A1_u

A1_o

Figure 9: Three views of building A1 without (A1 u) and with (A1 o) external tree25

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A2_u

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B1_u

B1_o

Figure 12: Three views of building B1 without (B1 u) and with (B1 o) external tree28

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B2_u

B2_o

Figure 13: Three views of building B2 without (B2 u) and with (B2 o) external tree29

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2.7 Parametric scheme

The daylighting performance was evaluated for all 480 unique combinations of buildingtype (x5), obstruction (x2), climate (x6) and orientation (x8). A schematic showing thevarious parameters is given in Figure 14.

A1 A2 A3 B1 B2

_u

unobstructed

_o

obstructed

Ten building types

Six climates

Eight orientations

X

X

=

480 combinations

Figure 14: Parametric scheme

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2.8 Generation of basic illuminance data

The following values were predicted for every calculation point at each 15 minute time-step and for each of the 480 permutations of building type (x5), obstruction (x2), climate(x6) and orientation (x8):

Direct sun illuminance - light that arrives at the calculation point directly from thesun.

Indirect sun illuminance - light from the sun that arrives at the calculation pointfollowing one or more reflections (internal and/or external).

Direct sky illuminance - light that arrives at the calculation point directly from thesky vault.

Indirect sky illuminance - light from the sky that arrives at the calculation pointfollowing one or more reflections (internal and/or external).

Note the daylight coefficient scheme was devised to predict daylight illuminance as fourseparate components. For this study however, total daylight illuminance (i.e. the sumof the four components) was the main quantity of interest. A total of approximately140 Gb of illuminance data were generated from the pre-computed daylight coefficients.Rather than store this considerable volume of data, the illuminance data were generatedon-the-fly from the daylight coefficients and the plots, summary metrics etc. producedfrom the temporarily stored illuminance data. The generation of illuminance data (frompre-computed DCs) and the production of plots etc. takes approximately 35 hours forall 480 combinations of building, climate and orientation. This timing was for use ofboth processors (100% utilization) on a twin-G5 (2.5 GHz, 4 Gb RAM) Mac Pro (UNIX)

workstation.

2.9 Standard daylight factor evaluation and comparison withthe DC computation

In addition to the climate-based analyses described above, the workplane daylight factorfor each of the ten window and configuration types was predicted using standard CIEovercast sky conditions [3].

Computationally, the prediction of the daylight coefficients for one building type is 145times more demanding than a daylight factor computation (for the DC scheme devised by

the author). The simulation time for the daylight factor computation was of the order ofa few minutes, with a DC computation requiring 145 times the computational effort, i.e.a number of hours. Note however that, for this study, each set of DCs were used to deriveapproximately 768,000 illuminance values (for every point on the workplane), i.e. 4,000(hrs) x 4 (15 min time-step) x 8 (orientations) x 6 (climates). With an efficient workflowestablished, the preparation for the DC simulation is not much greater than that requiredfor a DF simulation. For example, the following tasks need to be carried out for bothtypes of simulation: the Radiance model must be checked-over, viewpoints established,workplanes identified, testing of the ambient parameters to ensure reliable convergenceand low variance, etc.

As noted in the previous section, with a parametric climate-based evaluation, thelarger part of the user effort is with the post-processing and reduction of the voluminousgenerated illuminance data.

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3 Results: Graphical Data

3.1 Workplane plots: Distribution in UDI and cumulative illu-mination

The illuminance prediction data were processed to generate plots showing the distributionacross the workplane using false-colour for the following quantities:

Useful daylight illuminance - combined (100 to 2500 lux), supplementary (100to 500 lux), autonomous (500 to 2500 lux), fell-short (2500 lux).

Cumulative annual illuminance - total illuminance (i.e. sum of the four components)and plots for the individual components (units in klux hrs).

Cumulative monthly illuminance - total illuminance for each of the twelve months

(units in klux hrs).

Monthly direct sun illuminance - direct sun illuminance for each of the twelve months(units in klux hrs).

Each of the above four items constitutes a single page plot. Thus a total of 1920 (i.e. 4x 480) plots were generated to show these various values for all combinations of building,obstruction, orientation and climate. Those plots are supplied as four individual PDFfiles which accompany this report. Example workplane plots are shown below: Figures 15to 18.

3.1.1 UDI plots

The UDI plots (example: Figure 15) are annotated with the mean and median values forwhich each metric is achieved over the individual workplanes. For example, the meanvalue across workplane 1 for combined UDI was 3,201 hrs, and the median value was3,237 hrs. The closeness of the mean and median values indicates a near equal propensityfor low and high values about the mean. When the mean is markedly greater than themedian, this tends to indicate the presence of a localized hotspot of high values. Thiscan be seen on workplane 2 for the UDI-exceeded metric where there is a localized highoccurrence of illuminances greater than 2,500 lux (top left hand corner of wp2). This isthe cause of the localized coldspot for the UDI-combined metric. Note also that, whenthe median is markedly greater than the mean, this tends to indicate the presence of alocalized coldspot of low values as was the case for the UDI-combined metric on wp2.The mean value is the most readily understood. However for this application where thereare occasional hot or cold spots (but rarely both), the median is perhaps a more typicalvalue.

A compass icon (arrow pointing North) is used on all this series of plots to indicatethe absolute orientation of the building, i.e. the rotation from the default orientation (seeFigure 8).

3.1.2 Cumulative annual plots

The cumulative annual plots (example: Figure 16) are annotated in a similar fashion tothe UDI plots: the mean and median exposure on each workplane (in units of lux hrs) is

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given for the total illumination and the four components of illumination. The significanceor otherwise of differences between the mean and median values is, in principle, the sameas described above for the UDI metrics. Note that a logarithmic false-colour scale isused to show the large range in daylight exposures for the total daylight and the fourcomponents. The scale ranges from 50 klux hrs to 50,000 klux hrs. Zero values (whichgenerally occur only for the direct sun component) are shaded black.

Following the example UDI plot for the same case (Figure 15), the pattern in UDI-exceed on wp2 is clearly due to the high levels of direct sun exposure supplementedby smaller - but still significant - direct sky illumination. Note that, for this case, theprevailing levels of indirect illumination from the sun are comparable to the levels ofindirect illumination from the sky.

3.1.3 Cumulative monthly plots - total and direct sun

The cumulative monthly plots (example: Figures 17 and 18). The monthly plots are given

for illustrative purposes. Their purpose is to disclose the annual dynamic in daylightexposure. An example plot for total illumination is given in Figure 17, and one for directsun illumination is shown in Figure 18. Logarithmic scales are used again only now therange is from 10 klux hrs to 10,000 klux hrs.

3.1.4 A note on viewing the plots

The data generated for this parametric study is vast and not easy to assimilate. Further-more, in this early, exploratory stage of using climate-based daylight metrics, effectivemeans to reduce the data are only just emerging. The various plots presented in thesereports were devised specifically for this study to disclose as much information as possible

visually. They are the result of much experimentation and selection, however it is readilyacknowledged that they are open to further refinement as end-user requirements for theoutcomes of climate-based daylight studies becomes more defined.

The plots in this report are intended primarily for on-screen viewing. A screen 20inches (50 cm) or larger is preferable. It is recommended that the report PDFs areopened directly in Acrobat Reader and viewed in full-screen mode (i.e. control-L on aPC or command-L on a Mac). In particular, the order of presentation of the plots for thevarious cases has been carefully determined so that (in full-screen mode) the reader caneasily see the changes that result in, say, changing from the A1 u to the A2 u buildingvariants. Almost all of the plots are printed in landscape orientation to make best use of

the aspect ratio of the screen (Acrobat Reader will adjust automatically between portraitand landscape orientation during viewing). This arrangement also lends itself to directpresentation of the report PDFs using data projects.

The section, figure, table and reference numbers are enabled as hypertext links: aclick takes you to the linked item anywhere in the document. The back button in AcrobatReader (alt-left-arrow on a PC, command-left-arrow on a Mac) will return you the lastpage.

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A2_u 135 FRA_Nice

UDI: 100 < E < 2500 lux

3201 / 3237 [hrs]

3227 / 3460 [hrs]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

UDI fell-short: E < 100 lux

567 / 504 [hrs]

253 / 220 [hrs]

UDI supp: 100 < E < 500 lux

1580 / 1658 [hrs]

1026 / 937 [hrs]

hrs

0

1000

2000

3000

4000

North arrow

Mean / median

Figure 15: Workplane UDI plots

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A2_u 135 FRA_Nice

Total annual illumination [lux hrs]

3972 / 3032 [klux hrs]

7637 / 5065 [klux hrs]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

Indirect sun comp of TAI

698 / 687 [klux hrs]

1029 / 1038 [klux hrs]

Direct sky comp of TAI

1083 / 786 [klux hrs]

2572 / 1679 [klux hrs]

klux hrs

100

1000

10000

Logarithmic scale

Mean / median

Figure 16: Workplane cumulative annual illuminance plots

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A2_u 135 FRA_Nice

Monthly total illumination [klux hrs]

Jan Feb Mar Apr

May Jun Jul Aug

Sep Oct Nov Dec

Logarith

Figure 17: Workplane cumulative monthly illuminance plots

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A2_u 135 FRA_Nice

Monthly direct sun illumination [klux hrs]

Jan Feb Mar Apr

May Jun Jul Aug

Sep Oct Nov Dec

Logarith

Figure 18: Workplane cumulative direct sun illuminance plots

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3.2 Plots of UDI metrics based on mean and median values

The plots in this section are graphical presentations of some or all of the five UDI summarymetrics.

3.2.1 Polar plots

This series of plots (example: Figure 19) show the sensitivity of the mean and medianof the five UDI metrics with respect to building orientation. Individual plots for eachworkplane are shown alongside each other on the same page. A beaded line-style is usedfor the median value. The number of hours for each value is given by the radial distancefrom the centre. A series of concentric circles at 500 hour intervals are shown also.Displayed in this intuitive, compass arrangement the sensitivity to building orientation isreadily apparent.

Note that UDI-combined is relatively insensitive to building orientation. This is be-cause UDI-c can be achieved by illuminances down to 100 lux. Often these illuminancesresult from overcast skies which, of course, have a luminance distribution that is sym-metric with respect to rotation about the zenith axis. In contrast, the UDI-autonomousand UDI-supplementary metrics disclose the orientation-dependant contribution of illu-minance from the sun.

3.2.2 Individual cases across all climate and orientations

This series of plots (example: Figure 20) show the five UDI metrics as mean (or me-dian) values for daylight exposure across the workplane for each building variant in turn.Four of the UDI metrics are plotted as bars: UDI-fell-short; UDI-supplementary; UDI-

autonomous and UDI-exceeded. UDI-combined is plotted as an x symbol. For the meanvalue, UDI-combined is the sum of UDI-supplementary and UDI-autonomous which areshown as stacked bars, i.e. the combined magnitude is equivalent to UDI-combined andso the plot mark appears at the top of the stacked bars. However, there is no similarrelation for the median value and the plot mark for UDI-combined may appear above orbelow the top of the stacked bars.

The eight orientations for each climate are shown alongside each other running from0 to 315. Alternate orientations are shaded with a grey background for clarity andthere is a small gap between climates. The bar for UDI-autonomous is plotted at thebottom with the value for UDI-supplementary on top. This is to give a fixed baselineso that changes in UDI-autonomous can be more readily appreciated, since for a givenUDI-combined, it is generally preferable to have the greater part made of the higherilluminances in the UDI-autonomous range (i.e. 500 to 2,500 lux).

An overview of the sensitivity of UDI metrics to climate and orientation is readilyapparent from these plots. Also, from the series of these plots in the accompanyingreport, the sensitivity in all of these metrics from one building variant to the next islikewise readily disclosed.

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3.2.3 Change in UDI plots

This series of plots (example: Figure 21) show the difference in the UDI metrics thatresults from a change in the window configuration for either of the building types. Plotsfor the following changes in window and building configuration were produced:

A1 u to A2 u (i.e. no skylights to 2 x 1 skylights).

A2 u to A3 u (i.e. no 2 x 1 skylights to 2 x 3 skylights).


B1 u to B2 u (i.e. no skylights to 2 x 2 skylights).

A1 o to A2 o (i.e. no skylights to 2 x 1 skylights for obstructed case).


A2 u to A3 u (i.e. no 2 x 1 skylights to 2 x 3 skylights for obstructed case).

B1 o to B2 o (i.e. no skylights to 2 x 2 skylights for obstructed case).

Additionally, the following change in UDI plots were generated to quantify the effect ofintroducing obstruction for each particular building configuration.

A1 u to A1 o (i.e. unobstructed to obstructed).

A2 u to A2 o (i.e. unobstructed to obstructed).

A3 u to A3 o (i.e. unobstructed to obstructed). B1 u to B1 o (i.e. unobstructed to obstructed).

B2 u to B2 o (i.e. unobstructed to obstructed).

3.2.4 Summary

The polar, individual case and change in UDI metric plots are presented in an accompa-nying PDF to this report.

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ExamA2_u FRA_Nice

UDI Metrics [hrs]

Workplane 1

W E

N

S

500

1000

1500

2000

2500

3000

3500

4000

Workpla

W

N

SFell-short 2500lux

Mean Median

Figure 19: Polar plot showing sensitivity of UDI metrics to building orient

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Workplane: wp1 Case: A2_u Mean

0

1000

2000

3000

4000

N[

hrs]

000

090

180

270

CAN_Toro

000

090

180

270

DEU_Muni

000

090

180

270

FRA_Nice

000

090

180

270

GBR_Finn

000

090

180

270

USA_LosA

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Workplane: wp1 Case: A1_o to A2_o Mean

-1000

-500

0

500

1000

1500

2000

N[

hrs]

000

090

180

270

CAN_Toro

000

090

180

270

DEU_Muni

000

090

180

270

FRA_Nice

000

090

180

270

GBR_Finn

000

090

180

270

USA_LosA

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3.3 Overall graphical summary of change in UDI metrics

These four plots (Figure 22 to Figure 25) present an overview of the change in the UDImetrics as a percentage of the occupied year for the following change in window configu-rations:


A2 u to A3 u (i.e. no 2 x 1 skylights to 2 x 3 skylights).


B1 u to B2 u (i.e. no skylights to 2 x 2 skylights).



A2 u to A3 u (i.e. no 2 x 1 skylights to 2 x 3 skylights for obstructed case).

B1 o to B2 o (i.e. no skylights to 2 x 2 skylights for obstructed case).

The change in the mean and median values for the UDI metrics are shown for all 48combinations of climate and orientation. A triangle down symbol () is used for themean values and a triangle up symbol () for the median values. Additionally, theaverage of the 48 mean and the average of the 48 median values is shown using therespective symbol oversized with a vertical line above (for the mean) or below (for themedian) forming an arrow symbol.

These plots present a synoptic overview of the most significant features in the data,i.e. the change in daylighting performance due to the addition of skylights. Note thelarge scatter in the data, particularly for the the key UDI-a metric (i.e. occurrences ofilluminance in the range 500 to 2,500 lux). For example, the change from A1 u to A3 u(i.e. no skylights to 2 x 3 skylights) produces, for workplane 1, an overall mean increase inthe median values of approximately 33% (Figure 22). However, the range of the changegoes from approximately 19% to 47%. The standard deviation associated with thesevalues was 6%. Note the numerical data giving the overall mean values (i.e. the arrowsymbols used in the figures) together with the associated standard deviation, which is ameasure of the dispersion or scatter in the values, are listed in Table 14 to Table 17.

More than any other set, these four plots show how sensitive is the change in UDI

metrics to climate and orientation. This observation further demonstrates the sensitivityof UDI metrics to building configuration.

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-20 -10 0 10 20 30Change in UDI metrics as a % of the occupied year - wp1

Fell-short 250

Figure 22: Change in UDI metrics - Workplane: wp1 Cases: unobstructe

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Fell-short 250

Figure 23: Change in UDI metrics - Workplane: wp2 Cases: unobstructe

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Fell-short 250

Figure 24: Change in UDI metrics - Workplane: wp1 Cases: obstructed

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Fell-short 250

Figure 25: Change in UDI metrics - Workplane: wp2 Cases: obstructed

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3.4 Daylight factor plots

The last plots for this section are those showing the daylight factor predicted understandard CIE overcast sky conditions [3]. The same simulation engine used for theclimate-based predictions (i.e. Radiance) was used to generate the daylight factor values.

As with the plots of cumulative annual and monthly illumination, a logarithmic false-colour scale is used, Figure 26 to Figure 35. The plots are annotated as before with themean and median daylight factor values across each respective workplane.

The character of the daylight factor plots is markedly different from those for the allthe UDI metrics except perhaps for UDI exceeded. This is to be expected since both DFand UDI-e simply scale with increasing levels of illumination, the only difference beingthat UDI-e scales from a threshold value of 2,500 lux.

A illustration of the difference in the fundamental form of a UDI distribution comparedto a DF distribution for the same building is given in Figure 36. It is evident from theshading that the highest DF values are invariably regions where UDI-c is lowest (i.e.

negative correlation). Although there are regions showing positive correlation (indicatedon Figure 36) they can be closely positioned to regions showing the opposite. Recall alsothe large scatter in measures of UDI achieved, in particular the UDI-a metric (Figure 20).The magnitude and form of the UDI metrics varies greatly across climates and orientationsfor any one building type, where, of course, the DF distribution will be unchanging. Thusit seems that there is no potential for the DF to serve in any way as a proxy for any ofthe UDI achieved metrics (i.e. UDI-s, UDI-a or UDI-c).

Note also the marked difference between the wp1 and wp2 daylight factors for boththe mean and median values (A building).

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A1_u

Daylight factor [%]

1.4 / 0.9 [%]

3.4 / 2.2 [%]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

DF [%]

0.1

1.0

10.0

Figure 26: Daylight factor - building: A1 u

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A2_u

Daylight factor [%]

1.7 / 1.4 [%]

4.2 / 3.5 [%]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

DF [%]

0.1

1.0

10.0


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A3_u

Daylight factor [%]

3.2 / 2.9 [%]

5.8 / 5.5 [%]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

DF [%]

0.1

1.0

10.0


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A1_o

Daylight factor [%]

1.3 / 0.8 [%]

3.3 / 2.1 [%]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

DF [%]

0.1

1.0

10.0

Figure 29: Daylight factor - building: A1 o

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A2_o

Daylight factor [%]

1.6 / 1.3 [%]

3.9 / 3.2 [%]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

DF [%]

0.1

1.0

10.0


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A3_o

Daylight factor [%]

3.1 / 2.8 [%]

5.7 / 5.5 [%]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

DF [%]

0.1

1.0

10.0


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B1_u

Daylight factor [%]

2.0 / 0.8 [%]

3.4 / 1.5 [%]

6 8 10 12 14 16 18[m]

6

8

10

12

14

16DF [%]

0.1

1.0

10.0

Figure 32: Daylight factor - building: B1 u

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B2_u

Daylight factor [%]

2.5 / 1.7 [%]

3.4 / 1.5 [%]

6 8 10 12 14 16 18[m]

6

8

10

12

14

16DF [%]

0.1

1.0

10.0

Figure 33: Daylight factor - building: B2 u

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B1_o

Daylight factor [%]

2.0 / 0.8 [%]

3.4 / 1.5 [%]

6 8 10 12 14 16 18[m]

6

8

10

12

14

16DF [%]

0.1

1.0

10.0

Figure 34: Daylight factor - building: B1 o

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B2_o

Daylight factor [%]

2.5 / 1.7 [%]

3.4 / 1.5 [%]

6 8 10 12 14 16 18[m]

6

8

10

12

14

16DF [%]

0.1

1.0

10.0

Figure 35: Daylight factor - building: B2 o

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UDI: 100 < E < 2500 lux

3201 / 3237 [hrs]

3227 / 3460 [hrs]

2 4 6 8 10 12[m]

2

4

6

8

10

12

14

Daylight factor [%]

1.7 / 1.4 [%]

2 4 6[m]

2

4

6

8

10

12

14

-ve correlation

+ve correlation

-ve correlation

A2_u 135 FRA_Nice A2_u

Figure 36: Comparison of UDI-c with DF for case A2 u, orientation 135, climate

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4 Results: Tabular Data

The tabular data further reduces the data shown in the plots by taking the mean valueof various sets of UDI metrics in an attempt to summarize the overall performance ofthe building types and the performance changes in moving from one type to another, e.g.

from A1 u to A2 u. In addition to the mean, a value for the standard deviation (labelledStDev) is also given. The standard deviation is a measure of the dispersion in the valuesthat were used to calculate the mean.

The tables present UDI metrics averaged across all 48 climates and orientations. Thisdata reduction attempts to give a typical value for:

The daylighting performance of each of the ten building configurations.

The change in daylighting performance for any of eleven comparisons listed in Sec-tion 3.2.3.

The UDI metrics, of course, are dependant on climate and orientation. So these met-rics must be treated as approximate estimators of either the absolute or the change indaylighting performance - recall the scatter in the values shown in Figure 22 to Figure 25.

As noted, the standard deviation gives a measure of how much variation there wasin the values that were averaged to give a mean. The greater the standard deviationthe more uncertain the estimate of the mean (i.e. typical) value for the daylightingperformance metrics. The four groups are:

Group 1 - UDI metrics as number of hours occurred throughout the occupied periodfor each individual window and building configuration (Section 4.1).

Group 2 - UDI metrics as a percentage of the occupied period for each individualwindow and building configuration (Section 4.2).

Group 3 - Change in UDI metrics (number of hours occurred throughout the oc-cupied period) in switching from one window/building configuration to another(Section 4.3).

Group 4 - Change in UDI metrics (as a percentage of the occupied period) in switch-ing from one window/building configuration to another (Section 4.4).

4.1 Group 1: UDI metrics (hrs) across all climates and orien-tations

The first group of four tables (Table 2 to Table 5) show occurrence of the five UDI metricsaveraged across all 48 climates and orientations for each building type in turn:

Workplane 1, mean values (Table 2).

Workplane 1, median values (Table 3).

Workplane 2, mean values (Table 4).

Workplane 2, median values (Table 5).

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For illustration, consider the UDI metrics (mean values) for wp1 (Table 2) for buildingsA1 u, A2 u and A3 u. The mean (i.e. average) value of the occurrence of UDI-a (acrossall climates and orientations) was 816, 1132 and 1903 hours for buildings A1 u, A2 u andA3 u, respectively. The corresponding standard deviation values were 348, 418 and 301,indicating significant scatter about the mean. Nonetheless, the data shows significantincrease in the occurrence of UDI-a in going from building A1 u to A2 u to A3 u. Notealso the decrease in UDI-f: 860, 676 and 353 (for A1 u, A2 u and A3 u respectively).

UDI-c varies by a few hundred hours, and the corresponding values for UDI-s showthat the major shift in UDI metrics is a displacement of occurrences from UDI-s to themore preferable UDI-a range. This trend is even greater when the median values for theUDI metrics are considered (Table 3).

4.2 Group 2: UDI metrics (% occ yr) across all climates andorientations

The second group shows the same data as the first group normalised to the percentageof the occupied year, i.e. the hours occurrence values divided by 4380. Percentage valuesare rounded to the nearest whole number, Table 6 to Table 9). Repeating the illustrationgiven above, the occurrence of UDI-a (across all climates and orientations) was 19, 26and 43% of the occupied year for buildings A1 u, A2 u and A3 u, respectively. Thecorresponding standard deviation values were 8, 10 and 7%. The decrease in the UDI-fmetrics: from 20% (A1 u), 15% (A2 u) to 8% (A3 u).

As noted above, the trend is even greater when the median values for the UDI metricsare considered (Table 7). The tables giving the data as a percentage of the occupied yearare perhaps a more easily appreciable indicator of overall performance than those showing

the number of hours. Tables showing both the number of hours and the percentage of theoccupied year are given, however it is recommended that, for communication purposes,comparisons are made using the latter. Changes in daylighting performance are morereadily apparent in the tables that follow.

4.3 Group 3: Change in UDI metrics (hrs) across all climatesand orientations

This group of four tables show the changes in daylighting performance for the eleven casesnoted in Section 3.2.3. The data are presented in Table 10 to Table 13. A negative value

indicates a decrease in that metric in changing from, say, window configuration A1 u toA2 u. The first two tables show the changes based on the mean and median values forwp1, the following pair show the same for wp2.

4.4 Group 4: Change in UDI metrics (% occ yr) across all cli-mates and orientations

The fourth group shows the same data as the third group normalised to the percentageof the occupied year (Table 14 to Table 17). This group are perhaps the best to indicateoverall changes performance in daylighting performance between the eleven combinations

of window and configuration type considered.

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4.5 Summary: Salient features in the data

The discussion here is based mainly on the data presented in Table 14 to Table 17 (Sec-tion 4.4).

4.5.1 A1 u to A2 u (Workplane 1)

For this unobstructed case, the addition of a single skylight (i.e. A1 u to A2 u) results inthe following changes for the five UDI metrics (Table 14 and Table 15):

A decrease in UDI-f, i.e. -4% (mean values) and -7% (median values).

A decrease in UDI-s, i.e. -3% (mean values) and -5% (median values).

An increase in UDI-a, i.e. 7% (mean values) and 12% (median values).

An increase in UDI-c, i.e. 4% (mean values) and 7% (median values).

No change in UDI-e, i.e. 0% and 0%.

Thus, using the more typical median values, the addition of a single skylight results in a12% (of the occupied year) increase in the occurrence of illuminances in the range 500 to2,500 lux. This is accompanied with a 7% decrease in the occurrence of illuminances lessthan 100 lux, and a 5% decrease in the occurrence of illuminances in the range 100 to500 lux. There is no change in the occurrence of illuminances greater than 2,500 lux. Asnoted, these values are the average (or mean) change across all climates and orientations.Thus the change for some climate and orientation combinations will be greater and forothers it will be smaller - this applies to all the data in the tables.

4.5.2 A2 u to A3 u (Workplane 1)

Continuing with this unobstructed case, the addition of two further skylights (i.e. A2 uto A3 u) results in the following changes for the five UDI metrics (Table 14 and Table 15):

A decrease in UDI-f, i.e. -7% (mean values) and -7% (median values).

A decrease in UDI-s, i.e. -13% (mean values) and -15% (median values).

An increase in UDI-a, i.e. 18% (mean values) and 21% (median values).

An increase in UDI-c, i.e. 5% (mean values) and 4% (median values).

An increase in UDI-e, i.e. 3% (mean values) and 1% (median values).

Using again the more typical median values, the addition of a two further skylights resultsin a 21% (of the occupied year) increase in t

Climate-Based Daylight Analysis for Residential Buildings

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