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Lighting Research and

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 DOI: 10.1177/096032718001200102

1980 12: 7Lighting Research and TechnologyD.R.G. Hunt

Predicting artificial lighting use - a method based upon observed patterns of behaviour  

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Predicting artificial lighting use - a method based uponobserved patterns of behaviour

D. R. G. HUNT, MA, MCIBS

Mr Hunt was previously with the DOE Building ResearchEstablishment, Garston, Watford, Herts. The paper was receivedon 18 September 1979, and in revised form on 18 January 1980.

Summary A method is outlined for predicting the likely use of manually operatedlighting. The method is based upon patterns of switching behaviour observed in fieldstudies. A series of worked examples are given which demonstrate application of themethod to multi-person offices and open-plan teaching spaces. Two points of interestrevealed by the examples are the considerable effect that a midday switch-off has onthe predicted overall time that lights are on in a space; and the comparatively smalleffect that gradual afternoon switch-on of lights has on overall use.

1 Introduction

There are many occasions when it is important to be able topredict with reasonable accuracy the likely use of artificiallighting in a manually operated system; for instance, indetermining the energy balance of a prospective buildingearly in its desi~n’, or in assessing the cost-effectiveness ofautomatic lighting controls for an existing buildine,-. Atpresent, in the absence of appropriate data, estimates oflighting use have to be based on one or more of a number ofcrude assumptions: for example, that lights will be oncontinuously throughout a building’s occupied hours; orthat there will be no lighting use during summer months;or, commonly, that people behave like photoelectricswitches ensuring that the lights will be on only when thedaylight illuminance on the desks is below the design levelfor the artificial lighting. Assumptions such as these havehad to be made in the past because there have been noreliable data on how people use their lighting in practice.

Field work by Milbank et al4 provided overall figures oflighting use in office buildings, but these were not related todetailed occupancy or daylight conditions. Nevertheless,they did indicate that hours of lighting use could be muchhigher than those that would be expected from the third ofthe simple assumptions mentioned above.However, more recent field work carried out by BRE:I.;)

has gone a considerable way in remedying the state ofignorance about lighting use and has a realisticprediction method to be developed, based upon observedof occupants’ switching behaviour. Data onuse were collected from several schools and officesusing photography, Details the observedpatterns of behaviour have been reported in reference 5.The three main conclusions from the work are that:(1) usually either all, or none, of the lighting in a is inuse-it is rare for only pprt of the lighting to be on, but insuch cases discrimination in luminaire use depends to someextent on the position and layout of the switch panels;(2) the cycle of occupation of a space determines when

people switch the lights on and off. Switching activity isalmost entirely confined to the extremes of a period ofoccupation. People tend to switch on the lights-ifr~~ecieei-~raly at times when entering a space, and theyrarely switch off the lights until the space becomescompletely empty;(3) the probability of someone switching on the lights isrelated to the daylight illuminance at the darkest point inthe working area.These conclusions form the basis of the prediction

method which is outlined in this paper. For ease, themethod will be referred to as the ’behavioural method’ as itis based upon observed patterns of behaviour.

2 Basic method

2. The switching probability functions

As stated above, it was found that for any particularinstance, the probability of someone switching on lights in aspace is correlated with the minimum daylight illuminanceon the working plane. The probability function derivedfrom the field data is illustrated in Fig. 1. The curve wasconstructed using probit analysis’&dquo;, and the probit line hada correlation coefficient of 0.82; ie 67 per cent of the data’svariance was accounted for by the minimum working planeilluminances. For east computation in the work,however, the probit curve has been redefined in terms ofcurve parameters, as shown in Fig. 2. The fit to theoriginal probit curve is very close over the defined ranges.

in Ptg. 2 th-s cLE-ye ~,, = a ‘r~’ c/(’~-i .~~ exp b(x - m) ~where 100y = switching probability (per cent)

a = ―~.0175b = -4.0835c = 1.0361m= 1.8223x = logifi (minimum daylight illuminance level

in the working area, lux)

and y = I.l~ for xsO.843

y = 0.0 for x-2.818

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2.2 Switching probcability and occupancy

The first stage in determining lighting use by thebehavioural method is to decide at what times of dayswitching activity is likely to occur, according to theoccupation pattern of the space (see Conclusion 2 above).The frequency of each illuminance occurring during thatswitch-on period is then multiplied by the probability ofswitching for that illuminance (from Fig, 1) and theproducts summed over the whole illuminance range. Thusthis gives the overall switch-on probability for the specifiedperiod. Fig. 3 illustrates the method schematically. Theilluminance frequency curve, Fig. 3b, is derived in thefollowing way. Reference 6 gives details of the frequencieswith which various external daylight levels occur for arange of locations and time periods. These are converted tointernal illuminances by the process used to construct theoriginal probit switch-on curved iemin. daylight illum. in the working area (lux)= external total illuminance (lux) x 0.6 x orientationfactor x minimum daylight factor in the working area (percent)/100 (1)where 0.6 = mean ratio of diffuse/total illuminance, wherethe orientation factor = 1.20 for south-facing windows

1.04 for east-facing windowst.~ for west-facing windows0.77 for north-facing windows

Fig. l. Probability of someone turning artificial lighting onin a daylit spcze~-~e~~~ data from various sources.notes I A io E arefive different spaces; space C had two sets

of independently operated luminaires denoted nosl~-h~ ~r~~l l~-~5.

notes 2 each point represents nine observations.

Fig. .20 Probability switching function.

Fig. 3. Schematic representation of calculation procedure.

(these should be combined in suitable proportions forrooms with several walls glazed, or for windows facing inother than cardinal compass-point directions),and where the daylight factor is measured at, or calculatedfor, the working plane height.

Deviation of equation (1) and a discussion of the variousfactors is given in reference 5. (An orientation factorincluded as on average over the year the sky is relativelybrighter in some directions than in others and this affectsthe amount of a ¡room can be expected to receive.) 1

2,3 A s ~r~~~~~~~~ zteihod

Switch-on times, of course, depend upon the-occupationcycle of a and this varies amongst spaces of the sametype, as well as between different types of spaces.Therefore, for ease of use, the approach outlined above hasbeen used to construct a simple graphical method whichwill allow the user to obtain, for a typical year, probabilitiesof lights being switched on at different times of day, for arange of daylight factors. Reference 6 tabulates the sort ofilluminance data required to do this, and a simple computer rprogramme was written to perform the necessarycalculations.

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~’i,~. ~ f~~lct~i~~ freqttert~~ with which external total illuminances occur, for hourly periods during the day. taken c~~~c°r rt y~c~car ~a.sa whole. (Data for Kew 19~~-7.~~ 36.~-ci~y yecar; ~S7&dquo; ~pril-t~ct i~~cl.~.

Fig. 5. Fig. ~co~~H~/b~- 1300-1400h ~2~0-22~.

Calculations have been for two of year: anormal 365-day year and a typical school year. In terms ofdaylight availability, the former approximates to atypical year. With the is forschool holiday the details of which are gives inReference 6. In both cases, the daylight data used for thegraphs presented in this paper are based uponmeasurements of total made at Kew over the

10-year period, 1964-73. The data for the 365-day year isreproduced in Figs. 4 and 5.

Figs. 6, 7 and 8 show three different ways of presentingthe switch-on probabilities for the 365-day year. Theorientation factor can be regarded as a correction appliedto the daylight factor (see equation (1) above) and wherepossible has been allowed for by including additionaldaylight factor scales.

There are two interesting features of the graphs worthbrief comment before a series of worked examples aregiven to illustrate their application. Firstly, the probability~~ ~~~~~~~~~~-~~ the is strongly upon thelogarithm of the daylight (see, for example, Fig. ,5 ) .This is not altogether surprising since the basic switchingprobability curve (Fig. i) is a logarithmic function of theinternal illuminance. Secondly, except for a few hoursaround noon, the switching probability shows rapid changeswith time of day. This is a consequence of the rapid changesin daylight availability (Figs. 4 and 5) with time of day.

3 Worked

The application of ~gs. 6-8 is best explained by a series offworked examples:

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Fig. 6. Switch-on probabilities for 365-day year, hourlycontours.

F~g. 7. Daylight factor contours.

3.1 Example I

office; minimum daylight factor in theworking area = 1.0 per cent; west-facing windows andhence orientation factor = 1.0. Normal working hours,0830-1630h.

~,7&dquo;a,e,5,rst of the .calculation is to establishthe likely switching activity pattern. From the conclusionsof the field studies of muM-person offices’, it is reasonableto suppose that switch-ons would occur only at the start ofnormal working hours (0830h) and switch-offs only at theend of normal working hours (1630h)―assuming that theoffice remained continuously, but not necessarily fully,occupied between these times. Switching activity duringthe day would probably be small and in this example will beassumed to be negligible. Thus the lighting would beswitched on at the start of the working day, if required; andleft on until the end of the day.From Fig. 7, for the daylight factor of 1 per cent and

Fig. 8. Probability contours.

orientation factor of 1.0, the probability of switching on thelights at the start of the working day, 0830h, is 52 per cent,averaged over the year as a whole. Thus for an 8-hour day(0830-1630h), the average full-load lighting usage in theoffice would be0.52 x 8 = 4.2 hours per day.

This assumes that there is no discrimination in the use ofdifferent sets of luminaires, which follows from the first

, conclusion presented in Section 1.

3.2 Example 2

Same office as example 1, but normal working hours0900-1800h.These working hours might be typical of an office whose

staff worked flexible hours between 0900h and 1800h.

Again, it will be assumed that the office is occupied by atleast one person this period.The probability of switching on the lights at 0900h is 44

per ceni t f 6,7 the as a .w[zole (Hg. 7). This would lead toan average use of0.44 x 9 = 4.0 hours per day,if there were no further switching until the lightswere switched off at 1800h.

3.3 Example 3

Same office as example p but working hours of 0830h-1230h and 1330h-1630h.This pattern of working might arise in a multi-person

office whose occupants take a common lunch break from

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1230h to 1330h. During this hour it is assumed that theoffice is completely empty. Although no offices with thistype of occupation pattern were covered by the BRE fieldstudies--,, it is not unreasonable to take, once again, thethree conclusions set out in Section 1 as a guide in decidingthe likely switching activity pattern.

Since the office empties at lunch time it is probable that,if the lights had been on during the morning, they would beswitched off at 1230h.The 0830h switch-on probability is 52 per cent (Fig. 7),

which gives, for the four hours before lunch, a usage of0.52 x 4 = 2.1 hours.The 1330h switch-on probability is 25 per cent; if no

further switching activity took place until 1630h, this wouldresult in an afternoon usage of0.25 x 3 = 0.8 hours per day,making a total of 2.9 hours for the 7-hour day.

3.4 Within-day switch-ons

The last example assumed post-lunch switch-on activityat 1330h, and no further activity until the end of theworking day. But the question arises as to how realistic thisassumption is in view of the fact that the daylight levelduring the afternoon is gradually falling. Unfortunatelythere is at present no field data to call upon as evidence ofwhat might happen. But in the extreme case of theafternoon work session extending to midnight, it is obviousthat at some stage during this period the room occupantswill switch on the lighting, if it had not already beenswitched on immediately after lunch.The principles underlying an exact prediction of switch-

on rates in such circumstances are complicated, and so adiscussion of them has been postponed until Section 5.However, it is possible to estimate an upper limit to thelikely effect in the following way.

Fig. 7 indicates that for the room of example 3 beingnewly occupied at &dquo;1330h, the probability of a switch-on is25 per cent. Now suppose instead that the room was newlyoccupied at 1400h. Then from Fig. 7 the probability of aswitch-on at this time would be 27 per cent. Similarly, by1430h the probability would have risen to 31 per cent, andso on rising throughout the afternoon. It is notunreasonable, therefore, to use these values to determinethe probable upper limit of lighting use that would occurwith afternoon switch-ons. Thuslighting use between 1330h and 1400h would be Y2 x 0.25 =U.1~5h,lighting use between 1400h and 1430h would be 1/2 x 0.27 =&dquo; 9lighting use between 1430h and 1500h would be Y2 x 0.31 =0 155h,

giving a total usage 1330h and 1630h of I .0h.The eh~~iee c~f a iaalf-i~our i~;~~~~a1=~or the ? based

upon the linearity of the 1 per cent daylight factor contourin Fig. 7, since the process described is simply anintegration of the area under this contour between 1330hand 1630h. (The same method would be used, incidentally,for estimating lighting use in a room whose occupation wasrandomly intermittent. The only difference would be that,with the latter, the integration would be performed for thewhole of the working day, and the resulting lighting usethen multiplied by the overall probability of the room beingoccupied.)

Returning to the example, it is interesting to note that

the additional daily light usage resulting from this methodof allowing for afternoon switch-ons is only (1.2h/day, ie only7 per cent higher than the estimate based upon a singleafternoon switch-on. As the two approaches can beregarded as providing probable upper and lower limits forthe expected lighting use, it may be concluded that together rthey provide a fairly precise estimate of likely use.The same arguments about afternoon switch-ons could

also be applied to example 2 above (see Section 3.2). For itcan be noted from Fig. 7 that although as a result of the0900h switching activity there is a 44 per cent chance of thelights being on at any time during the day, by 1600h there isa 46 per cent probability of someone switching on lights ifentering a physically identical office but one whoseworking day started at 1600h.So late afternoon switching activity is at least a possibility

that ought to be considered, and indeed a very smallamount was observed in the field studies.An integration procedure similar to that described above

can be used to provide an estimate of the probablemaximum effect on overall light usage. It seems reasonableto commence the integration around the time that theprobability given by Fig. 7 (for the appropriate orientation-corrected daylight factor) exceeds the probability of lightsbeing on in the room already. In this case the appropriatetime is 1600h:the switch-on probability at 1600h is 46 per cent

at 1630h is 51 per centat 1700h is 56 per cent

and at 1730h is 62 per cent.Thus the lighting use between 1600h and 1800h is given by1/2 x (0.46 + 0.51 + 0.56 + 0.62) = 1. lh per day.The light usage between 0900h and 1600h is still given by

7 x 0.44 = 3. ih/dayg making a total daily use of 3. + 1.1 =4.2h/day.Once again, the estimated effect of late afternoon

switch-ons on daily usage is small, adding only 5 per cent tothe previously calculated usage, ~.43h/day.

It is interesting to note in passing the equivalentswitching rate implied by this method. For the presentexample it is:0.46 - 0.44 = 0.02 times per day between 1600h and 1630h0.51 - 0.46 = 0.05 times per day between 1630h and 1700h0.56 - 0.51 = 0.05 times per day between 1700h and 1730h0.62 - 0.56 = 0.06 times per day between 1730h and 1800hie a total of 0. 18 times/day on average.This figure is considerably higher than any of those

observed for the multi-person offices of the field studies.However, the comparison is not strictly valid because ofdifferences in daylight penetrations and office hours.Nevertheless it further support to the that theintegration method provides an estimate of the maximumlighting use likely to occur in practice.

3.5 Comments on results

Two conclusions can be drawn from the results outlinedso far. Firstly, gradual afternoon switching on of the lightsis likely to result in only very small additions to the overalldaily light use. Secondly, a comparison of the results fromexamples 1 and 3 shows that switching the lights off atlunchtime has a considerable effect in reducing their dailyusage. The fall in light use is 3 per cent (4.2h/day reducedto 2.9h/day) although the reduction in the length of timethe office is occupied is only t2% per cent (8h/day to

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7h/day). If the lights are switched off at midday, less thanone third of the daily use is likely to occur during theafternoon.

3.6 Results for rooms with different daylight penetration

Table 1 gives results for the same occupation patterns asthe worked examples, but for offices with higher (A) andlower (B) daylight penetrations. Office B is the office of theworked examples. (These are not to be confused with thereal spaces of Fig. 1 ~.One point of interest arising from table 1 is that for the

lightest office (office A), the predicted light use for the 9hday is less than that for the 8h day, although the differencein starting times is only half an hour. This is also true foroffice B if there is no late afternoon switching activity.

4 Testing the method

Confidence in the prediction method will be increased ifits results agree with further, independent fieldmeasurements (ie with light use data other than that used toderive the method). Unfortunately, none of sufficientdetail is available for offices but data have been collectedfor two open-plan school spaces, as reported in reference 5.In each case the switching facilities were complex and sothe information collected did not readily lend itself to theprobit method of analysis.

It was possible, however, to examine the temporalfeatures of the switching activity which, as expected, wereclosely related to the occupation cycles of the spaces.Lights were generally switched on only at the start of theschool day and, if on, not switched off until the end of theafternoon school. Little switching activity occurred duringthe central part of the day.

’

One of the teaching spaces was composed of three linkedareas, facing east, south and west respectively. No light usedata were collected for the south wing but information wasobtained for each of the other two from time-lapse camerasplaced in them. So in predicting the lighting use for theseother two areas, it is reasonable to take a value of 1.0 forthe orientation factor. The minimum daylight factor was0.3 per cent. Fig. 9 displays switching probabilities for atypical school year (the data shows only slight deviationsfrom its 365-day year equivalent. Fig. 7) and gives the tl~3tlhswitch-on probability as 82 per cent. The measured lightusage between 0900h and 1600h, averaged between theeast and west wings of the open-plan teaching space, was6. lh/day, ie 87 per cent of the 7h period. This closeagreement suggests that, had it been possible to analyse theschool’s data more fully, they would have been consistentwith the probit curve derived from data for the otherrooms.

The second of the two open-plan teaching spaces wasalso composed of several inter-connected areas. Temporalaspects of the light use data were analysed according tovarious switching zones. The basic pattern of switchingbehaviour was similar to that described above. Table 2

Table 1. Predicted light use for three multi-person offices.

TaMe 20 ~’~~~~~~ ~~~~~~v~1 and observed ~F twching space.

Note: Predicted switch-on rates taken from Eg. 9.

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~‘i~. ~..~~.eteh-on ~~r~b~~ilitaes fc~r° cx typacal schoc~l ~~~~ar° (seer~f 5 for c~et~~ls~~

gives details of a comparison between the predicted 0830hswitch-on rates and the overall lighting use between 0900hand 1600h. Again, agreement between the measured andpredicted values is reasonably good, three of the four zonesgiving agreement within 7 per cent of measured dailyusage. p

5 Further discussion of ’during-day’ switching

Before concluding, it is appropriate to return to thequestion of ’during-day’ switching which was treatedsomewhat pragmatically in Section 3.4. In practice-atleast for the reasonably wide range of examples given-theproblem is not very important in terms of its effect onoverall lighting usage. But in some circumstances it may besignificant and so the basic model ought to be developedfurther to cope with such cases. For this to be done, morefield studies are necessary. The sort of data that is requiredwould be obtained, for example, from monitoring a multi-person office with a well-defined mid-aay switch-off and inwhich for a substantial proportion of the year it was toodark to work under daylight alone by the end of theworking day-and also, of course, in which for a substantialpart of the year daylight sufficed immediately after lunch.Figs. 6-8 could perhaps be used to define the characteristicsof such an office M detail.However, it is sufficient in this paper simply to oueiine

some of the questions that such a field study should set outto resolve.Of first importance would be to determine whether the

’during-day’ criterion for switching on the lighting is thesame as the start-of-day criterion. It might be reasonable topropose some sort of ’inertia’ argument to account for therelative infrequency of switch-ons during periods ofoccupation. Several factors might be involved:;, eg areluctance of occupants to take action which might disturbor distract others in the space; a disinclination to interrupt

work in order to move to the light switch (which in mostinstallations is situated away from work stations, by thedoor); and the good adaptation of the eye to graduallydecreasing light levels. All of these factors would tend tocreate a different, less sensitive switching curve than thatfor the start of a period of occupation.However, evidence from the field studies conducted so

far in multi-person offices tends not to support the idea of adifferent switching criterion. Although the duringoccupation switch-on data were somewhat sparse, theirinclusion alongside the start of occupation data did not leadto significant heterogeneity in analysis.

It seems more likely that the observed scarcity of during-day switch-ons in the multi-person offices is primarily dueto the fact that if lights were ’needed’, it is very likely thatthey were already on as a result of the action taken at thebeginning of the day. The frequency with which thedaylight level fell substantially below its start of occupationlevel at some point during the working day seems to havebeen small.This raises another interesting aspect of the problem.

Returning to the schematic presentation of Fig. 3, it is clearthat for during-day switching the appropriate daylightfrequency distribution (b) to use is one which not onlyrefers to a specific time of day but also excludes all theexternal daylight levels corresponding to occasions whenthe artificial lighting is used. Now in practice, the resultingdistributions will be very different from those presented inFigs. 4 and 5, because the new ones will exclude many ofthe lower illuminance values. Consequently, it is not validto use Figs. 6-8 to obtain absolute probabilities for during-day switching activity. The daylight distributions thatwould be required, in fact, are ones of the form: given anexternal illuminance level Ei at time ti (so that theprobability of lights being switched on at that time andconsequently being on at later times can be determined)what is the probability of having an illuminance level E~ atsome later time t2 (so that the switch-on probability for thattime can be calculated from a daylight distribution thatexcludes levels for which lights are already on). This sort ofdistribution could be obtained in principle, but in practice itwould involve a considerable amount of computation toobtain and be unwieldy to use.

Until such time as this is done, and in fact until there isfurther field data on which to base refinements to themodel, it is recommended that the integration methoddescribed in Section 3.4 be used. It was shown there thatthe results it yields are sufficiently accurate for mostapplications.

6 ~~~ei~~a~~

A method has been outlined for predicting the likely useof manually operated systems. As it is uponobserved patterns of switching behaviour it offers a morerealistic approach than many of the assumptions used atpresent in, for instance, energy balance calculations.A series of worked examples have been given to

demonstrate application of the method. The effects onlighting use of a midday switch-off, gradual afternoonswitch-ons and variations in working-day start times are allillustrated and discussed.The method has been tested against the small quantity of

independent data available and agreement betweenpredicted and measured use is encouraging. Further,

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validation of the model will help to establish its reliabilitvand value. Two field studies,-,’ of a short duration haverecently been carried out in Canada using techniquessimilar to those employed by BRE. Results from a multi-person office and a school classroom confirm the temporalfeatures of switching behaviour observed in the BREstudies, but so far no analysis has been published of thedaylight levels at which people switched on the lights.

It is hoped that, by having outlined the method at thisearly stage in its development, others will be stimulated tocollect further field data and analyse it in such a way thatthe results can be used to confirm the behavioural approachto predicting lighting use outlined in this paper. Particularemphasis should be given to rooms with daylightpenetrations and occupancy cycles not covered by thestudies&dquo; that form the basis of the switching model.Amongst these, studies of single person offices and multi-person offices empty at lunchtime would be of mostinterest.Current BRE studies on the variation of daylight factor

with orientation and external illuminance&dquo; could also helpto refine the method. Other studies are also planned whichwill attempt to define the design implications for buildingsif estimates for lighting use are made using this behaviouralapproach rather than the hitherto ad hoc assumptions.

Acknowledgements

The work described has been carried out as part of theresearch programme of the Building Research

Establishment of the Department of the Environment andis published by permission of the Director.

References’ Jones, W. P., Built Form and Energy Needs. Proc.

Conference on Energy Conservation and EnergyManagement in Building, CICC (1975).

2 Crisp, V. H. C., Energy conservation in buildings: apreliminary study of automatic daylight control ofartificial lighting. BRE Current Paper 20/77 (1977).

3 Hunt. D. R. G., and Cockram, A. H., Field studies ofthe use of artificial lighting in offices. BRE CurrentPaper 47/78 (1978).

4 Milbank, N. O. , Dowdall, J. P., and Slater, A.,Investigation of maintenance and energy costs forservices in office buildings, JIHVE. 39, 145 (1971), (alsoavailable as BRS Current Paper 38/71).

3 Hunt, D. R. G., The use of artificial lighting in relationto daylight levels and occupancy, Building andEnvironment, 14, (1), 521 (1979).

6 Hunt, D. R. G., Availability of daylight, BRE (1979).7 Levy, A. W., Pattern of lighting use in a large open-plan

office, Building Research Note 129, Nat Res Council ofCanada (1978).

8 Levy, A. W., Pattern of lighting use in a schoolclassroom, Building Research Note 132, Nat ResCouncil of Canada (1978).

9 Collins, J. B., Crisp, V. H. C., Hunt, D. R. G., andLynes, J. A., Availability of daylight and its use toconserve energy used for electric lighting, Proc CIESession, Kyoto (1979).

10 Finney, D. J., Probit analysis. Cambridge (1964).

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