A visualization of Continuous Daylight Autonomy (cDA)

As an (electrical) lighting designer, daylighting is exciting to me for a lot of reasons.  There are energy conservation reasons, of course–40% of the electricity consumption in commercial spaces is lighting, and daylight harvesting is a mostly untapped method of reducing that sum.  There is evidence that a connection to the outside world is beneficial to the happiness and productivity of the occupants, such as the research conducted by the Heschong Mahone Group on classrooms and commercial office spaces.  And, artistically, there’s a tremendous potential to create dynamic sculptures using the sun and building form, and to contribute to the narrative of the architecture.

I just got back from attending the Daylighting Institute at the 2012 Lightfair, which if you have a chance to go is really worth your time.  As the LEED sustainable building program becomes the default for high-profile projects, it is pushing daylighting design from the provenance of academic research and a few specialty firms out into the mainstream of standard architectural practice.

Many of the seminars this year revolved around the various daylighting metrics available, whether moment-in-time based metrics such as that found in LEED 2.2, to dynamic metrics such as daylight autonomy (DA), useful daylight illuminace (UDI), and spatial daylight autonomy (sDA).  I thought a quick reference guide to the various ways of measuring daylight within the space, with examples, might come in handy for people like myself that are trying to get a handle on all this.  The metrics are in approximate order of how established they are within the design community, with metrics that are still under active development like Spatial Daylight Autonomy towards the end.  I’ve also created a generic example space to help explain the concepts.

The example space, rendered in AGI

For purposes of visualization, the above space a 32′x32′ classroom, with a large window along one wall.  While I’ll be referring to this as a classroom, I wanted results that are generally applicable as a basis of comparison, so I have excluded federal holidays from the occupancy profile, but not the summer and winter holidays and the like.  Maybe a lot of kids get detention in this particular class :)

The Focal Point Verve IV, used for the electric lighting

The reflectances are the typical 80% ceiling, 50% walls, and 20% floors.  I am using the Focal Point Verve IV with 3xT5 lamping and CDR option for the electric lighting, along the line of what the Collaborative for High Performing Schools (CHPS) recommends (outboard lamps on for purposes of calculations).

Graphic of Daylight Factor, calculated in Daysim

Daylight Factor (DF): The ratio of internal illuminances to exterior illuminances, under a CIE overcast sky.  For example, if you have 2,000 fc exterior, and 20fc average interior, that’s a 2% DF.  This is the earliest standard of daylighting, developed as a legal basis in 19th century Britain for determining when a new structure would intrude on the daylighting of another.  A DF greater than 2% is considered adequate, 2-5% is considered well daylit.

Pros:

• It actually works pretty well if you are somewhere with prevailing foggy or cloudy conditions, such as, well, London for example.
• Simple to calculate and can be generated without any additional effort in AGI32.

Cons:

• DF gives the same results independent of orientation, time of day, and climate.  So for a north-facing window in a foggy climate, it would be of some use, whereas for a western exposure in Phoenix, it would give *very* inaccurate results.

Our example:  Daysim unfortunately doesn’t give calculation summaries, but AGI gives us 2% total for the space.  You can see the distribution above.  So that would be considered acceptably daylit.  However, looking at the distribution of daylight factor percentage from Daysim above, supplementary electric lighting would be needed for the row of fixtures furthest from the window on almost every day.

LEED 2009, Prescriptive Method: Calculate the ratio of window area (above 30″) to floor area and multiply by the visible light transmittance (VLT).  Achieve a value between .150 and .180.

.150 < VLT * WFR < .180

I mention this only in passing because it’s not really a design tool so much as a LEED compliance metric, but our floor area is 32×32 = 1120 sq. ft., the window surface area is 96 sq. ft., giving a Window to Floor ratio (WFR) of .087.  So unless there is a glass type that will transmit more light that it receives, this space would not qualify for LEED credits via the prescriptive method.

The classroom at 9am on the equinox. Values below 25fc are highlighted in blue.

LEED 2009, Simulation Method:  ”Demonstrate through computer simulations that 75% or more of all regularly occupied spaces areas achieve daylight illuminance levels of a minimum of 25 footcandles (fc) and a maximum of 500 fc in a clear sky condition on September 21 at 9 a.m. and 3 p.m. Areas with illuminance levels below or above the range do not comply. However, designs that incorporate view-preserving automated shades for glare control may demonstrate compliance for only the minimum 25 fc illuminance level.”

Pros:

• I think if you want a pass-fail metric, this gives a lot of value relative to its fairly simple nature. There are a lot of high-performing daylit spaces that would not meet this metric, but you are probably doing more right than wrong if you do.

Cons:

• For most spaces, it’s very unlikely that you can get this LEED point without skylights.
• Automated shades, which would be necessary for most buildings with windows, are expensive and fussy to get working correctly.
• Since this is run under clear sky conditions, it’s more of a worst case scenario than typical of conditions at a given project site.

Our example:  Calculated values for the different orientations (in AGI32) are as follows:

Hours of Electric Lighting per Year

So this space would not qualify for LEED points using either method.

Dynamic Metrics: the metrics after this point are dynamic, i.e. rather than simulating a few scenarios as typical, they take a weather file with hourly measurements of solar irradiances, and attempt to return a comprehensive result for the performance of the building over the course of a typical year.

For more on this subject, I highly recommend “Dynamic daylight performance metrics for sustainable building design” by Reinhart, Mardaljevic, and Rogers.  This is probably a good time to say that this article does not represent original research on my part, I’m just summarizing notes from lectures I’ve attended on the subject along with other reading I’ve done.

Daylight Autonomy (DA): an annual analysis of the fraction of occupied time that the daylight levels exceed a specified target illuminance.   So for every point in the space, what fraction of the time could an occupant work without supplemental electric lighting?  Put another way, DA as a metric has three inputs: climate-based daylighting levels over an entire year, the actual hours of operation of the facility, and the specific desired lighting level.

Pros:

• DA gives an intuitive look at how well daylight will penetrate into the space.
• Good for determining which fixtures would benefit from automatic daylight harvesting.
• Takes into account the hours of actual operation and real weather conditions at the site.

Cons:

• Since there’s no upper limit on the allowed illuminance levels, poorly performing spaces with direct sunlight could do quite well on this metric.  To put it another way, a glass box would have a DA of 1.0, but that wouldn’t be a very comfortable space to be in, as it would essentially be a greenhouse.

Our example:  Daylighting is present under the first row of fixtures often enough that it might make sense to put them on a switching control as they’ll likely be off during all daylight hours and on during all occupied night hours.  For the second row, it would appear that supplemental electric lighting would be needed during most of the day.

Continuous Daylight Autonomy (cDA): As per above, but partial credit is given for daylighting that is less than the target illuminance.  For example, if your threshold value is 30fc, and you (somehow) have 15fc at a specific point 100% of operating hours, your cDA for that point would be .500

Pros:

• I think cDA is intended for use as a compliance metric more than a design tool.  While a space that has enough daylight to reduce electric lighting loads would do pretty well on cDA, problem times and climate scenarios would not necessarily be apparent.

Cons:

• cDA is less suited for comparing two potential designs, because if you have .100 for an area, it’s not clear whether that’s because there were 30 fc 10% of the time, or 3fc 100% of the time, or what proportion in between.

Our example:  We can resolve that ambiguity above by comparing the cDA map against the DA map in the prior section.   For example, in the side of the room nearest to the windows, the map looks about the same from DA to cDA, so when there *is* daylight, it’s sufficient for task lighting.  Conversely, for the lower half away from the windows, there’s hardly ever light above 30fc as the DA map is solid blue, but there is pretty consistently a fraction of the necessary light, so dimming controls would be advantageous for that row of lighting.

Useful Daylight Illuminance (UDI): Another variation on Daylight Autonomy, UDI attempts to penalize for direct sunlight that falls into the space, on the theory that people will find this glary and distracting.  UDI is a set of three numbers for every point in the space, the percentage of time that a point was below a minimum threshold, between a useful minimum and maximum value, and above a maximum value, which could result in glare or thermal discomfort.  Often 10fc is used as the lower bound of useful illuminance, and 250fc on the upper bound.

Pros:

• Most of the advantages of DA, but an additional dimension for glare and thermal discomfort.
• Good for comparing the performance of two design variations.

Direct sun exposure causing veiling glare on the whiteboard (window faces West in this simulation).

Daylight Glare Probability (DGP): Glare is when the ratio of luminances within the field of view of an observer exceeds the comfortable dynamic range of the eye, such that the highlights appear blindingly white, and details are obscured by stray light bouncing around in the eye.  It’s a hard metric to quantify because it’s very dependent on the observer (personal preference and age), the position relative to sources and the specific task being performed, and can arise in a number of ways such as reflections from the sun or luminance ratios between windows and adjacent surfaces.

The reference method for glare assessment calculations is to generate an HDR image for the position in question for every daylight hour of the year, and then compare the luminance ratios and adjacencies to come up with a probability that an average observer at that point would find it objectionable.  But, doing this for even a simple space with one observer position entails thousands of hours of calculation, so in practice a simpler method is necessary. Daysim is capable of assessing an optimized algorithm DGP for a defined viewpoint.

This is very much an area of active research, but a proposed metric for DGP in Wienold 2009, Dynamic Daylight Glare Evaluation, is below.

Pros:

• Predicts potential glare issues directly by evaluating luminance ratios, rather than a proxy metric such as aSE.

Cons:

• Even with the simplified/optimized algorithm used in Daysim, it’s a very time-consuming calculation.
• The optimized algorithm does not incorporate peak glare  from sources such as specular reflection or direct sunlight exposure on reflective surfaces, if I understand it right.
• Results are only for the location and orientation tested.
• Conceptually opaque, i.e. it’s hard to make a correspondence between the DGP number and what the specific

Our example:  I wanted to try orienting the classroom to a west-facing window, and then run DGP for a ‘student’ position and a ‘teacher’ position in front of the whiteboard, but I couldn’t get Daysim to return a DGP in a reasonable amount of time.  The computer ran for a full day without finishing (3.0 GHz dual-core processor, although it appears to only use one core for this calculation).  I think that any metric that takes longer than 24 hours to calculate has a severely limited utility as a design tool.

Slide from a recent lecture by Lisa Heschong and Kevin Van Den Wymelenberg

Annual Sunlight Exposure (aSE): is the number of hours per year at a given point where direct sun is incident on the surface.  It has a great deal of import generally for heating and cooling, and is well-established in use for displaying artwork that the sun would damage.  There are IES recommendations for how much annual light exposure artwork of various types may tolerate, in lux-hours.

In the forthcoming IES LM-83, aSE is defined as the percentage of square footage that has direct sunlight for more than 250 hours a year.  Direct sunlight is abstracted as >100fc incorporating glazing materials, but not sky light, bounce light (radiosity), and not incorporating operable shades.

Pros:

• Fast to calculate, since it doesn’t require radiosity solutions or luminance maps for an observer point.
• Useful as a design tool, since it gives a good handle on where the problem areas might be of a design.
• Incorporates potential issues of thermal discomfort.

Cons:

• Does not address issues of glare due to specular reflections or veiling glare from high luminance ratios.  aSE not, strictly speaking, a glare metric at all, it’s just a proxy that has been found to predict glare discomfort in many cases.

Our example: Since this standard is still under development, it’s not surprising that neither Daysim nor AGI has a pre-built configuration for it.  For the north-facing window, the aSE would be 0 of course, since no direct light from the sun (not sky) would pass through the window.  But, above is a slide from a recent lecture I attended with a similar space, at least in the sense that it has large north-facing windows.  The A-B-C classification is a field study of how highly the occupants rate the quality of the daylight in the space vs. predicted quality via sDA and aSE, somehow?  There were 150 slides, so some of it went by before I could really absorb it.

Spatial Daylight Autonomy (sDA): is another metric that is under active development, but it will described in IES LM-83, which is expected to be released later this year (2012).  sDA is the percentage of area that is above 30fc 50% of the time or more during business hours.

Pros:

• Unlike Daylight Autonomy which returns an array of data points for every location in the space, it returns a single number for the space.
• Experimentally verified to predict occupant satisfaction, using a large study of 61 sites (forthcoming study by HMG group).

Cons:

• I don’t know if this is a drawback since it’s by design, but it’s worth pointing out that sDA does not incorporate glare or direct sun exposure– it is intended that aSE would be evaluated separately.

 In Conclusion:

There are a number of discrete tasks that the daylighting consultant has to perform, such as comparing design variants for performance, selection of glazing and operable shading materials, and communication of the impact of design choices to other members of the design team.  All of these metrics can contribute to some of those tasks, and which metric is optimal can depend greatly on the project geometry and location.

It’s an exciting time to be working in this field, because due to voluntary sustainable building initiatives such as LEED, there is a great deal of visibility for the benefits of daylighting, and I think we’re on the cusp of it becoming a design fundamental for most projects.  Already, daylighting contribution is routinely considered in some project types, such as classrooms and airport concourses.

While my expertise is in electric lighting, I’ve been working on learning about this subject, because I think that the next generation of lighting designers will also be daylight consultants– the interactions between daylight and electric lighting are profound, and lighting designers are or at any rate should be already comfortable with running photometric simulations.  I hope this was interesting and useful to you, please do drop me a line or comment if there’s anything you’d like to add!  (or correct, of course)

Here are some pictures from a production of Fool for Love that I designed with Boxcar Theater Company.  What’s notable about this production is that I made all of the lighting fixtures.  As a site-specific piece, we didn’t want to introduce anything into the performance space that didn’t belong there, such as theatrical lighting equipment.  So I modified practical lighting fixtures to work for theatrical purposes.  The result was a performance that was truly without a proscenium frame, more raw and intimate than theater, even good theater, is usually.  Additional pictures after the break.

The purpose-built theater as it appeared in preshow

Click to enlarge.

Here is something you can do on Friday:  Now playing at the Hyde Street Studios with Boxcar Theater company, True West by Sam Shepard.  This is the first of two plays I’m designing with Boxcar Theater company, and it’s a great show, and I’m very happy with the design.  I’ll do a writeup for it on my portfolio site.

True West runs through April 7 and I hope you can make it.  More information here.

So my dad is a classic car enthusiast, and he has a ’63 Lincoln slabside and a ’56 Lincoln Mark II. It’s not really germane to this post, but let’s have a picture of that, shall we?

So anyway, he edits the newsletter for the club he’s in, the many-syllabled Lincoln Continental Owner’s Club, Texas Gulf Coast Region.  As people his age go, he’s fairly computer savvy, but not really a desktop publishing guy, so his method of publication has been to assemble the stories in Word, and then create a PDF from that with links to flickr albums and email that to everyone in the club.  It’s a functional solution, but I thought an upgrade was in order, so for Christmas I built him a blog of his very own, now up at www.thecontinentalstar.com

If you want to stop by, there are lots of pictures from prior events, as well as listings of cars for sale, directions to the next meet, etc. etc. etc.

In my prior post about daylighting analysis, I focused on a ‘representative points’ approach, i.e. taking as typical a mid-morning and mid-afternoon time on the vernal equinox, along with perhaps some bounding points on the winter and summer solstice, and extrapolate the quantity and quality of natural light from there.  I was interested to know if a more granular approach would confirm the validity of this method, and what other useful information it might yield besides.

I set the computer up to run a calculation for every 30 minutes, on thirty day intervals throughout the year, for CIE Cloudy, Partly Cloudy, and Clear skies.   That made for some 600 radiosity calculations in all, so after queuing all that up, I let my desktop run for about two weeks straight.

Untold processor cycles later, I had hundreds of text files that looked like this:

So this was not a terribly useful format for the data to be in what I wanted to do, because I was interested in the behavior of the average and minima of, for example, the “Books Adult Stack 2″ fields as they span all the files that I have, not the value of any one particular field.   Also, the files were named things like sample_3.txt and my computer crashed several times during all this so I had several files with the same name etc. etc. etc. agggghhhh.

Anyway, after trying any number of other things, I wrote some bash scripts to collate and assemble the data for me.  I’m told that future releases of AGI will have more sophisticated data output options, but in hopes they come in handy for someone else, here’s the script I wrote:

#!/bin/bash

echo "Usage: sh extract.sh filename.txt (or *.txt)"

echo ""

echo "Script does two things: creates csv files from a tab delimited input with the pertinent fields in the file names, and collates those parameters along with other data to a central csv file called extract.csv"

echo ""



for filenam in "$@"

do



    #extract the values of the Conditions, Date, and Time fields

    # to a string, with an underbar delimiter

    timestring=$(awk 'BEGIN{ FS="\t"; RS="\r\n"}

        /Conditions/ {print $2"_"}

        /Date/ {print $2"_"}

        /Time/ {print $2}

        END {}' $filenam)



#put the fields you want to match in the /.../, and the fields you want

#returned after the print command

    datastring=$(awk 'BEGIN{ FS="\t"; RS="\r\n"}

        /Main Room RHS/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Floor/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Books Adult Stack 2/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Check in 1_Surface_5/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Computer Area/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Adult Reading Workplane/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Childrens reading area/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Adult Reading Workplane/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Entry Vestibule/ {printf "_"$1"_"$3"_"$4"_"$5}

        /Adult Stack 1/ {printf "_"$1"_"$3"_"$4"_"$5}

        END {}' $filenam)



    #read it out to the terminal, just for confirmation

    echo $timestring



    filenamappend=$(echo $timestring  | tr ":" ".")

    echo $filenamappend



    #extract the base name without extension for concatenation

    dfile=`basename $filenam`

    dfile=${dfile%.*t}



    #create a .txt file with the conditions in the filename

    tr "\\t" "," < $filenam > ${dfile}"_"${filenamappend}.csv



    #send it to one csv file, changing the delimiter to commas

    echo $timestring$datastring | tr "_" "," >> extract.csv



done

Also, so that I could make pretty graphs.  Like this one here:

Graph Porn.

This portrays average footcandle levels on the vertical surface of a section of stacks in adult section.  The levels are very uniform throughout the day (x axis) and year (y axis), except during the winter months, where for a few hours each day there is a large spike in the values.  Since those values are an order of magnitude larger than anything else, they must be caused by direct sunlight.  Since they are occurring in the winter months, I would guess that they are due to sunlight through the windows, rather than the skylights above.  A quick peek at the full-color rendering confirms this.

That behavior would have been discovered if daylighting analysis was done for the ‘worst-case’ summer/winter solstices, but not if only data for the ‘typical’ equinoxes were generated.  It’s important to look at those outliers, is what this graph is telling us.

Rendering of direct sun at low winter angles.

Variability of sky conditions:  Some daylighting programs can take as an input a weather file containing average sky luminances based on weather station measurements.  This is the preferred method for daylighting analysis, but day-by-day sky conditions can vary from cloudless to totally overcast on any day of the year, particularly in Berkeley where this library is to be located.

A question I had was how much numerical variation there is in interior lighting levels between a sunny day and a cloudy day, just as a practical rule-of-thumb.  An average sky condition might be less meaningful if there’s more than an order of magnitude difference between lighting levels on a sunny and cloudy day, since we are concerned about the occupant experience on atypical days as well.

Variation in lighting levels between sunny and overcast sky conditions

This may be somewhat difficult to visualize, but we are interested in the ratio of average lighting levels between clear sky days and cloudy days, i.e. the space over which weather would affect measured lighting levels.  I found a fairly insignificant ratio of up around 2:1 or 3:1 during times when there is no direct sun in the space, and a fairly significant ratio of 5:1 up to 11:1 when direct sun enters the space.  For example, the average lighting levels mid-day in the winter varied from 42fc for overcast days up to 470fc for sunny days (!).  A close-up of that data:

Average daily lighting levels under several sky conditions on the winter solstice.

The take-away would seem to be that unless there is penetration by direct sun into the space, a typical climate can be used to approximate any weather condition.  Notably, the greatest variation in lighting levels due to sky condition was at mid-day points where there was more than enough light in the space in any case.

Cost vs. benefits of dimming:  One question that came up in the design process was the cost/benefit of dimming vs. switched daylight harvesting.  For example, for the computer use area in the main room, we might desire a minimum of 30fc on the task plane 30″ above the floor, as this is sufficient to do the sort of paper tasks that might be performed at this location.   The electric lighting could be controlled by a switched relay, in which case it’s either on or off, or a dimmer, in which case it could provide some of the light but not all of it for cases where there is some daylight but not enough to read.

The thing is, there can be a considerable cost adder for dimming control, particularly for fluorescent lighting.  So the question of whether dimming controls are worthwhile can be stated: for how many hours per year is there some daylight, but less than our target 30fc minimum?

Lighting levels throughout the year, with values > 0fc but < 30fc in orange

In the above graph, time of day is plotted vs. time of year, with contour lines for footcandle levels.  The area where dimming would save energy over switched daylight harvesting is shaded in orange, and it works out to about an hour in the morning and an hour at night.  However, the expected operating hours of the library are expected to be 10am to 6pm most days, so it really only comes into play in the winter.  There are some additional potential benefits to dimming controls as well, but on the basis of this I recommended that we do dimming controls on the LED fixtures where it could be had easily and inexpensively, and forgo them for most of the fluorescent fixtures.

Forecasting total power consumption:  Lastly, I was wanting to get a handle on the real energy usage over the course of the year, as opposed to the total connected load.  AGI unfortunately can’t take real climate data for the area as an input, so I used the partly cloudy data set as a rough-and-ready average value.

For each of the twenty or so calculation grids within the space, I identified a target illuminance level for each and totaled up the number of hours each year where daylight contribution was less than acceptable.  I used the current operating hours of 10am-6pm four days a week, 12am-8pm two days a week, and closed on Sundays (plus an extra half hour after closing for cleanup).

Hours of Electric Lighting per Year

The lighting energy draw of the main room is about 4000W with everything on, so the Real Lighting Power Density is something like 47.2 KWh/yr.  I’m not using a particularly granular or methodical approach here, because in any case the end result is a vanishingly small amount of yearly energy use due to electric lighting.  To give some context, according to DOE figures, the office space averages about 6 KWh per year for lighting, per foot.  The energy savings to be had by good daylighting design are clearly orders of magnitude greater than the energy savings due to efficient electric lighting design.  That’s something to contemplate at a time when code-mandated electric lighting design power densities have reached a point of diminishing returns.

Conclusion:  On a typical project, we don’t have time to run a comprehensive study like this, of course.  I think that looking at this data, the methodology of generating data points for a morning and afternoon time at the equinox, with bounding points at the winter and summer solstices is validated.  I would suggest that for the solstices clear sky conditions be run regardless of the prevailing sky conditions, to generate ‘worst case’ scenarios.  One factor that would make this study less general is that the space is primarily lit from above, with skylights, and the angles are such that there is almost never direct sunlight entering the space.  For spaces with larger windows, I would expect sun angle and sky condition to produce much more variation in lighting levels.  On the other hand, adjustable shading technology is much more available for windows than skylights.

Hope you enjoyed this, it took me months to find the time to generate and assemble and analyze the data.  If you have any questions or comments, please feel free to get in touch or drop me a line below!

I found some planks that had been discarded on O’Farrell street (picture here), and decided to make a cutting board from them.  I’ve only got about 2′ x 3′ of counter area in my comically tiny San Francisco apartment anyway, so I’m essentially replacing all of the counter space.  That suits me because I feel like cutting boards are kind of a sub-optimal solution, in that you’d ideally want the entire workspace to be a cutting area.

My original idea for this was that I was going to stain specific pieces in a semi-random pattern as in this pre-visualization rendering I did:

But that ended up being impractical, first because you would have to stain the pieces before assembling, and after you assemble them there would have to be additional planing and sanding (as things turned out, a *lot* of planing and sanding).  Also important: I couldn’t find a stain or dye that I was sure wouldn’t leach into the food.  I could have achieved the effect by using two different types of wood, but the goal for this project was to use up the free wood I found.

Detail of three of the planks being glued together (in my vise, which I also made :) ). There are internal dowels that run the length of the board for strength and rigidity.

I still wanted that linear semi-random texture, so after some experimentation what I ended up doing was this: I left the top surface untreated except for mineral oil, and used a dye rather than a stain to lay the color all over the back.  Then I went over it with a few layers of water to raise the grain as much as possible.  When I sanded it down flat again, the parts that were raised were completely removed, and so I got a really strong zebra effect from the natural wood color versus the stained dark blue/black parts that weren’t removed.

The finish came out so well that it’s kind of a pity that it’s on the bottom and sides where it’s less visible.

You can see on the end there’s a raised section, so that the end of the board actually overhangs the sink slightly.  That’s great for cleanup because I just wipe everything into the sink– it’s too large and unwieldy to pick up and clean, otherwise.

I’m really happy with the way it all turned out, and it’s such a pleasure to use something every day that you’ve made.  I was a little worried about the durability of pine versus a hardwood for this application, but as long as I treat it with mineral oil it seems to hold up fine.  If I was going to do it all again, I would definitely use a powered industrial planer—it took me a lot of time to get everything flush and true by hand.

I'm a pretty terrible cook, actually.

These are some quick reference guides I made for my dad, for Christmas.  I am posting it now because June is the next month after December, clearly.  So my dad has been hampered in working on his classic cars and rental properties with their myriad electrical problems because he’s never been exposed to the fundamentals of electricity.  I figured that what he needed in lieu of a formal physics course was a quick reference that he could refer to as needed.

Q is for Charge

I is for Current

V is for Voltage

R is for Resistance

P is for Power

I put together some original copy and graphics for each of the major concepts in electricity that were relevant to what he needs to do.  To make the final product, I then laminated each card and included a set of mini Sharpies to make notes on the back or circle key points or whatever.

There’s a lot more that could be done here from a graphic design standpoint, and as I was formatting them for web I found some errors in the material, but for a three-day project I’m pretty happy with how they came out.  PDFs, if you want to print out a set for yourself, are here, and you can download a zip of the photoshop files if you want to make some of your own here.

Also, can I parenthetically remark here that wikipedia is awful?  It’s written to sound like an encyclopedia, not to explain things clearly and accessibly, so it’s completely opaque on any conceptually difficult topic.  Yet when I want to really want to research something in depth, it only has the same shallow gloss of the subject that you’d find in a dead-tree encyclopedia.  Critiques of wikipedia usually revolve around concerns about quality of citations and bias, which misses the meat of the issue, that as a reference guide to anything except pop culture trivia it sucks.  Exhaustive scholarship on that Josh Whedon series though!

South exposure glazing at 3pm, noon, and 9am.

Prologue: I have been wanting for  to better educate myself on daylighting design and analysis, and its coordination with traditional lighting design.  Here in California, we have one of the most aggressive energy efficiency codes in the country, Title 24.  While this and voluntary measures such as LEED have driven impressive technological advancements in smart, lean building, we’re now a point of diminishing returns because, quite simply, most of the low-hanging fruit is gone.  With emerging technologies such as LED lighting still less efficient than good fluorescent lighting (and at three times the cost), there’s no source-efficiency cavalry around the corner.

So not to sound like a goddamn hippie here, but it happens we have a free light source that is available all day long.  But before you break out the celebratory granola, consider this: for a typical commercial interior, we might desire 35 footcandles so as to be bright enough to read printed text but not so bright as to wash out our computer monitors.  Direct sun lighting levels might reach 10,000 fc on a sunny day.  Also, the sun moves from east to west, sky conditions vary, and so on.  All of this makes architectural daylighting design a fascinating and bedeviling practice.  I’m still woefully under-qualified to address that topic generally, but I’ve done an extensive daylighting analysis for the West Berkeley Library project I wrote about earlier with an eye to practical application to lighting design, and I present my preliminary findings in this post.

In a daylighting analysis, the software takes parameters such as geographical location, time of day and year, and sky conditions, and simulates the amount and quality of light that would pass through each window and then bounces the light off each surface in the room until an accuracy threshold is reached.  In the example at the top of this post, the light in the room includes contributions from direct sunlight, the ambient light of the sky dome, and the bounce light from secondary reflections.

Knowing how much usable daylight will be present is in some ways less crucial for lighting design than many other disciplines such as glazing selection, since in any case the lighting designer will still have to provide a quality electric lighting solution for when the facility is used after dark.  But daylighting calculations can certainly inform and enhance a lighting design, in particular in controls design.  I would generally like to know:

• Which luminaires in the space might benefit from switching control via daylight harvesting.
• How many hours a year a luminaire would be completely off if controlled by daylight harvesting, for purposes of cost-benefit analysis.
• The value added benefit of dimming or multi-level switching via daylight harvesting control; i.e. how many hours per year daylight would be provide some but not all of the necessary lighting in the space.
• Any design gotchas, things of the form ‘well, I didn’t know mauve was going to look so goddamn pink.’

How it’s usually done: the standard methodology of daylighting analysis consists of:

1. Set up a daylight study for some representative times, e.g. 9am and 3pm on the spring equinox, and perhaps also some outliers such as the summer and winter solstice;

2. Let the computer crunch numbers over the weekend;

3. On Monday, make coffee, look at the results, then make a bunch of assumptions about how the space will behave under myriad other conditions;

4. Adjust the design and repeat, time allowing.

Which is not to say that the above is a bad method.  Any daylight study is necessarily a gross simplification.  As an incremental increase in accuracy can easily result in a doubling in calculation time, it’s clear that the art here is in choosing what shortcuts we can take, what details will not contribute to the accuracy of the calculation and may safely be omitted.

False color rendering of the children's section, 3pm.

September 22nd, 9am: the date used in every daylighting calculation is day of the spring/fall equinox, when the length of day and night are equal.  Also, it’s the day when the angle of the sun is halfway between the steepest angle on the summer equinox, (about 75 degrees above the horizon in San Francisco) to the shallowest midday angle on the winter solstice (about 30 degrees above the horizon).

We think of the sun as being overhead, but for most of us in the norther hemisphere an average angle for the entire year might be 30 degrees above the horizon– more horizontal than vertical.  On the equinoxes, the sun is only above 45 degrees for about four hours.  So as a typical angle of sunlight, a time in the mid-morning and/or mid-afternoon are going to be a better representation than noon.

The data: the above figure shows the location of the key calculation point grids in the library, and the table below it shows the values.

For reference purposes, the lighting levels under totally electric lighting conditions are:

Interior lighting design in 100 words: If you’re not familiar with footcandle levels, 30-50 footcandles at the task plane is about the level you need for comfortable reading, depending on age, size of text, contrast of text to page, etc.  For general ambient lighting, 10fc is sufficient for simple orientation and tasks, e.g. locating the keys in your apartment and not walking into things.   For public or commercial spaces, you want to keep the uniformity to less than 10:1 maximum to minimum (lower is more uniform), to avoid uncomfortably bright spots or very dark shadows. On the other hand, having the entire space lit to one even level is fatiguing to your eyes and makes the space feel sterile and uninviting.  In daylit spaces, 500fc is about where people will take action to reduce lighting levels, e.g. by closing the blinds.

For the vernal equinox, 9 am, clear skies, we have:

And for the same day, clear skies, 3pm:

Conclusions: the daylighting consultant has done a fairly remarkable job of allowing usable amounts of diffuse light into the space, for one.  Most daylighting scenarios include footcandle readings in the thousands in areas of direct sunlight, with a precipitous drop off as you go away from the windows.  For larger spaces, it can require more electric lighting to make the rest of the space feel well lit in contrast to the daylight zones than if there were no daylight at all.  To have this much usable daylight throughout the space takes some real artistry.

In general, lighting levels look pretty good to me, and I think the supplemental electric lighting in this part of the library will be minimal.  The main tasks the space should support are:

• General orientation and navigation through the space: at least 10 fc in the large calculation areas of “Main room floor” and “Main room RHS.”
• Browsing stacks: 30 vertical footcandles to read the spines of books and in particular the call numbers.  This would be the stacks calc areas such as “Adult Stacks 1″ and “Stacks long wall.”
• Children’s reading area: children’s books tend to have larger text, and children’s eyes need much less light– a 65 year old needs twice as much light to accomplish the same task as a 20 year old.  25fc should be plenty.
• Adult reading area: 50fc is about what I’m aiming for here, to support a wide range of text sizes, text to page contrasts, and age of the reader.
• Computer area: lower ambient lighting levels actually help with visibility of the screen, but we also need to support some reading tasks.  35fc is a good number.  The calculated lighting levels of this area are quite a bit lower than this especially in the morning, so I’m expecting the indirect wall wash in this area to be on most of the day.
• Entry Foyer: In an analogous way to its function as a buffer zone for climate control, this area should provide a transition zone from outdoor levels, which might be in the thousands of footcandles, to the interior lighting levels, which are generally less than 100 fc.  From the first table, the electric lighting in that room is good for about 30fc, so I’m expecting to run it during the day to bring the foyer lighting levels up.  At night it will do the same thing in reverse, providing a transition between the high interior lighting levels to the lower outdoor lighting levels, so I’ll need the ability to dim or bi-level switch here.

There are a few areas, such as the the adult reading area in the morning, that show very high contrast ratios.  It might be desirable to turn on the electric lighting in these areas, even though the average lighting levels are quite high, in order to reduce the contrast.  This is where renderings are really helpful, since it can be difficult to tell what’s really happening in the space from a few data points.

9am view of the adult reading area

From the rendering it’s easy to see that the high contrast ratios are due to the occasional patch of direct sun that makes it through the shades on the exterior facade, and is not a cause for concern.  Similarly, for the right side of the space:

The high values are clearly from a patch of direct sun at the end of the hallway.  On the other hand, I’ll probably want to give the librarians at the information counter to the left of the image control over the downlights overhead, as that area might feel a little dark depending on weather conditions.

Since I have the luxury of time for the West Berkeley library project, I thought it would be instructive to generate a much more complete data set, for purposes of comparison.  I’m interested here in what a larger data set might tell me that the ‘key points’ data set above doesn’t, i.e. whether we’re making the right simplifications when we do daylighting calculations.  To that end, I’ve generated about 20,000 data points for this project, and I’m sorting and parsing and analyzing that mound of data now.

Please feel free to drop me a line or leave a comment if you have questions or feedback!