Daylighting metrics are methods for measuring the quantities of daylight in a space during a period of time. More and more, metrics are becoming the dominant means by which daylighting in a space is evaluated. With the imminent adoption of the International Green Construction Code and other codes mandating daylighting, the use of metrics will become even more integrated into the daylighting evaluation of buildings. While evolving analysis tools provide new and exciting capabilities, they also present new challenges to the designer or consultant.
Metrics have the inherent benefit of providing better information on the performance of a space than traditional rule-of-thumb methods. They are fast, adaptable, and instill confidence in the client, and the flexibility of digital modeling allows many variations of a design to be tested quickly at early stages of the design process. Unlike rules-of-thumb, metrics are more easily capable of evaluating non-orthogonal spaces, and they are becoming more accessible as more and more software provides daylighting analysis tools. And if that were not enough, increasingly, clients demand to see statistics and false-color grids in order to be convinced that their building will perform well, achieve credits, or meet codes.
But, like all things, metrics have downsides: metrics can be deceptively convenient. It seems as if it should be relatively easy to just build or import an architectural computer model into a simulation program and run the metric, but this is not so. Each software has its own rules for producing correct output. These include ways in which geometry should be modeled, whether or not to include a ground plane and how to define materials. Different lighting simulation engines have different ray-tracing methods (e.g. backwards versus forwards), and different simulation settings. The same basic variable likely has a completely different name from one program to another, and of course, the software interfaces are different – certain programs allow control over lighting variables, while other programs keep the user from accessing or modifying those variables.
Conversely, one benefit to the increased focus on daylighting metrics is their increasing accessibility. Plug-ins like DIVA-for-Rhino and the su2rad script allow widely used softwares like Rhino and Sketchup to interface with Radiance, the premiere calculation engine. While this overall accessibility is positive because it allows daylighting analysis to be employed more freely, making it more of a player in design decisions, it also makes education about the proper use of those metrics much more important.
The first step in understanding metrics is to know what metrics currently exist and what information they can provide. A useful guide to daylighting terminology was provided by Kevin Van Den Wymelenberg in an Architectural Lighting article in 2008, where he defines several of the daylighting metrics currently most in use today: Illuminance, Daylight Factor (DF), Daylight Autonomy (DA), Continuous Daylight Autonomy (CDA), and Useful Daylight Illuminance (UDI).
As a quick overview, the main distinction between various metrics is between the so-called “point-in-time” (Illuminance, Luminance) and annual, climate-based calculations (DA, CDA, UDI). Point-in-time calculations measure light levels at a specific date and time, under a specific sky condition. These calculations are more intuitive because they mimic how we experience the world: we see the light levels change from one moment to another. Annual or climate-based calculations, on the other hand, use weather data to simulate lighting levels over the length of an entire year. As such, they are more comprehensive than point-in-time metrics, but are also a more abstract, less intuitive way of measuring lighting. While they provide a more comprehensive performance evaluation, they may not show as clearly why one scheme performs better than another. Daylight factor, which originated in the cloudy climate of Britain, is neither point-in-time nor annual, as it uses an evenly illuminated (overcast) sky condition to measure interior-to-exterior light ratios.
Once designers have some idea of which type of calculation to use, they are faced with the issue of whether or not they can use it. Currently, the majority of lighting calculation software provides only illuminance and luminance calculations on a point-in-time level (for example, a clear day on September 21st at 9:00 AM). In general, there is a movement towards using annual, climate-based calculations rather than point-in-time, but the critical issue is that most commonly used daylighting programs do not support climate-based metrics. At present, 3dsMax and AGi32 only calculate illuminance and luminance (point-in-time). Daysim is the only widely used lighting engine which can perform the annual calculations.
The given metric may not really deliver answers to the questions at hand. From an architect and owner’s perspective, there are usually several critical questions posed to the consultant about daylighting. The first two are: how often will we be able to dim or turn off the electric light, and how will daylighting affect thermal performance? Currently, there is no good metric to directly answer those questions. Christoph Reinhart and Jan Wienold have developed one metric, called Daylight Availability, which perhaps comes the closest. In their paper “The Daylighting Dashboard – A Simulation-Based Design Analysis for Daylit Spaces,” they document the metric. It combines DA (Daylight Autonomy) and UDI (Useful Daylight Illuminance), and shows, in one false-color grid, the assessment of areas that are likely to be overlit (requiring shading), well lit by daylight alone, or partially daylit (requiring supplemental electric light). It is possible that this metric, or one like it, could fill the void.
The final part in the daylighting metrics process is the output. Once a metric has been chosen and run, the programs produce either a rendered image, a false-color image, or a grid of numbers as a result. The job of the daylight analyst is done, right? Of course not. This step can be the most challenging of all. Expressing daylighting analysis results in an intelligible way, and presenting them to a client can be difficult. There is no formula for the best way to do it, and it often comes down to what the particular situation requires. The fact is that it is difficult to synthesize in a single image the variability of lighting conditions over the day and year, and when multiple design options like shading devices, materials, or orientations are added, the complexity expands proportionally. Given this, there is a tendency to become metric-happy and produce copious studies for different times and under different conditions; this often overwhelms the client who, unfamiliar with the format, may barely understand a single false-color grid, let alone a set. Even for sophisticated daylighting designers, the useful conclusions may be hidden in the sheer mass of output.
Outputs produced with DIVA-for-Rhino
There is no single metric which can answer all questions; each provides only part of the story. Annual calculations provide information about lighting levels, but not about glare, thermal costs, or aesthetics. One idea beginning to gain acceptance as a solution is the concept of a “dashboard”. Dashboards, as laid out by Reinhart and Wienold, are meant to show summary results of many metrics in a single side-by-side view, although, it should be noted, that synthesis is still left to the consultant.
Reinhart and Wienold, “Daylighting Dashboard” concept image
Lastly, the architect’s and owner’s question, “What will it look like?” still prevails. False-color grids and numbers don’t read as quickly as does an image, and after all, a large part of the value of daylighting design is improving the visual quality of the space. Images may contain the least amount of hard data, but they tend to go the furthest in illustrating daylighting concepts to clients.
As we enter this new phase of daylighting analysis, it is important to know the strengths and shortcomings of each metric and to be informed as to how to properly use them. The increased predominance of the computer does not change the fact that it is the designer who must know how to use the tools, how to understand the results, and how to effectively communicate the results to team members and clients.
Radiance Visualization using DIVA-for-Rhino
Images credit: Kera Lagios(1-3,5), Christoph Reinhart and Jan Wienold (4)