Lux Lighting System
Modular, Generative, and Human Centered Task Track Lighting
Artificial lighting is an important part of the indoor environment. Designers have faced challenges when designing lighting, and the process is usually more technical than creative. There are online tools that can make the process easier, but most do not consider human behavior which is what this product aims to achieve by incorporating a generative design aspect.
Problem Statement
There is often a misfit between the lighting provided and the lighting necessary to support the tasks occurring in a room. This results in either excessive or insufficient lighting scenarios and an inefficient use of light.
Solution
Our solution is a track lighting system that is modular, generative, and human centered catered to specific individuals’ needs.
Research Findings
1. Human performance
Lighting is essential for human performance — color and brightness can affect mood, and lighting adaptability is necessary to accommodate different spaces and activities.
4. Artificial Intelligence
AI has the ability to reduce the need for human intervention. For designers, a generative process can create self-learning algorithms that can potentially serve as “expert-on-sights,” increasing the efficiency and success of lighting design.
2. Universal design
Principles of universal design state that designs should be flexible in use, be simple and intuitive, have perceptible information, have tolerance for error, and require low physical effort.
5. Existing solutions
Existing solutions for lighting optimization take into account daylighting data, floor plan analysis, and they also have the ability to calculate luminance. But one area that is missing is taking into consideration human behavior.
3. Requirements and standards
There is no one set of standards that determine absolute lux values, but there are general guidelines that describe ranges of lux values a space should have in order to optimize lighting.
Design & Development
Inputs & Outputs
The Grasshopper 3D algorithm we used to develop our tool, contains two main components of 1) developing the shape of the track and 2) finding the optimal light setting for the track. The flow chart below shows the inputs and outputs of the algorithm, and also breaks down the algorithm’s process.
Algorithm
01 Identify Task Points
Identify task points from behavioral mapping
02 Ambient Setting
Find minimum number of lights needed based on the area of a room and the type of task
03 Secondary Task Setting
For each task area:
Identify required lux for each specific task and number of lights needed to meet them, define the circumference of the task area, evenly distribute lights within the task area on the track , evenly distribute remaining lights along the remainder of the track
04 Evaluation
Calculate minimum, maximum, and average light intensities across the surface of the task area
05 Connection
Connect all the pieces of the algorithm to create the final system
Heat Map
In our base case of a studio apartment, we identified four distinct areas/tasks that require different lighting requirements: kitchen, desk, closet, and bed.
The heat maps visualize the lighting effect of the different light setting based on the task and location within the space.
Machine Learning Through Mobile App
Another quality of the lighting system that we see being implemented in the future, is incorporating machine learning and artificial intelligence. After installation, the lighting system is automatically set to standard settings, however, lighting needs vary from user to user. Incorporation of AI can help in accommodating the lighting needs of as many users as possible.
Through an app, the user is able to have manual control over the lighting system, adjusting lighting levels to settings that best suit them for different tasks. By reading this user input over a period of time, our lighting system would learn the preferences of the user and potentially gain the ability to predict user behavior and preference.
mobile and desktop view of preliminary design for the task track light
As generative design tools become more commonplace, designers will also have to adjust to incorporate these tools and begin creating their own systems to make designing easier. Soon, we will not be painstakingly drawing each wall and window, but having artificial intelligent systems generating the next best space. However, this future is far from reality, and we must take small steps to develop such a smart system. As designers, we hope that our algorithm can help to broaden the generative lighting design solutions