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NORDIC LIGHT & COLOUR
85
series of instantaneously occurring daylight illuminances since
these cannot be reliably inferred from cumulative values. As
noted, evaluations should span an entire year. There is some
debate as to whether the daily time period of analysis should
be all daylit hours, which vary in length with the seasons, a
standardised “working day” of 8, 10 or 12 hours, or the actual
occupancy pattern of the space. Different purposes are likely to
favour different daily analysis periods.
There are some long-standing daylight prediction methods that
make use of climate data to estimate either instantaneous or
cumulative illuminance. For example, the thermal simulation
program DOE-2 has featured a daylight prediction module for
over twenty years (Winkelmann and Selkowitz, 1985). These
methods however do not explicitly simulate the transport of
light in a space and instead employ various crude approxima-
tions. Furthermore, they are generally limited to very simple
building geometry with basic material properties (Koti and
Addison, 2007). In contrast, climate-based daylight modelling
refers to techniques that use lighting simulation proper. Ad-
ditionally, there should be few significant limitations on either
the complexity of the building geometry or the properties of
the reflecting and transparent materials used since high levels
of realism are necessary to adequately simulate the daylit
luminous environment for the majority of real building designs,
e.g. ‘Daylighting the New York Times Building’ (Lee et al., 2005)
(Figure 7).
Daylight Metrics
A metric is some mathematical combination of (potentially
disparate) measurements and/or dimensions and/or conditions
represented on a continuous scale (Mardaljevic et al., 2009a). A
metric may not be directly measurable in the field. A criteria is
a demarcation on that metric scale that determines if some-
thing passes or qualifies, e.g. three-quarters of the workspace
area achieves a 2% daylight factor. The purpose of a metric is
to combine various factors that will successfully predict better
or worse performance outcomes, and so inform decision mak-
ing. Performance may be described by more than one metric,
i.e. it is not necessary to combine all significant factors into
one metric. The most useful metrics have an intuitive meaning
for their users and can also be directly measured for validation.
This implies a preference for simplicity so they can be intuitive-
ly understood, and a direct relation to measurable outcomes
made. When metrics are sufficiently refined and understood
and their predictive capabilities validated, then performance
criteria can be set for various guidelines and recommenda-
tions.
As has been noted, metrics founded on the daylight factor are
relatively straightforward since there is no time-varying com-
ponent and so they simply report on: the DF value at a point;
some average DF value across a workplane; or perhaps some
measure of uniformity of the DF across the workplane. Metrics
founded on climate-based modelling are potentially far more
complex since the simulations output illuminance data at each
time-step for every point in the space. Thus, for all daylight
hours in the year, a climatebased simulation would output ap-
proximately 4380 values for every calculation point considered.
And potentially several times this number if the simulations
were run at a shorter time-step to, say, better resolve the pro-
gression of the solar patch across the internal space.
Various climate-based daylight metrics have been formulated
since the emergence of climate-based modelling in the late
1990s. These metrics are being investigated by daylighting re-
searchers in order to determine their potential to reliably char-
acterise daylight in buildings for the purpose of discriminating
between ‘good’, ‘bad’ and ‘mediocre’ de- signs (Reinhart et al.,
2006) (Mardaljevic et al., 2009a). One of the more straightfor-
ward climate-based metrics is daylight autonomy (DA) (Rein-
hart and Walkenhorst, 2001). The DA metric determines the
annual occurrence (within, say, working hours) of illuminances
above a stated design level illuminance, e.g. 300 or 500 lux.
Qualitatively, plots of DA appear similar to those for DFs since
both are proportional to the available illuminance
– regardless of the external conditions (i.e. time-varying for
climate-based and static, overcast for the daylight factor). It is
well known however that occupants prefer day- light illumi-
nation not to exceed certain levels, although it is not clear
what precisely those levels are since occupants vary in their
responses. The ‘useful daylight illuminance’ (UDI) metric was
formulated as a means to reduce the voluminous time-series
data from a climate-based simulation to a form that is of com-
parative interpretative simplicity to the daylight factor method,
but which nevertheless preserves a great deal of the signifi-
cant information content of the illuminance time-series. The
UDI metric informs on the occurrence of illuminances in the
range that occupants either prefer or tolerate together with the
propensity for excessive levels of daylight that are associated
with occupant discomfort and unwanted solar gain (Mar-
daljevic, 2006). Thus useful daylight illuminance is more firmly
grounded on human factors than metrics which determine only
sufficiency for task.
Achieved UDI is defined as the annual occurrence of illumi-
nances across the work plane that are within a range con-
sidered “useful” by occupants. The range considered “useful”
is based on a survey of reports of occupant preferences and