Chaotic Lighting Control System for Adaptive Occupant Experience
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Solution Overview
Problem
Traditional lighting control systems are limited in their ability to dynamically adjust lighting conditions to enhance occupant experience, particularly in typical spaces like homes and commercial buildings, and lack adaptability to user interactions and environmental influences.
Innovation Solution
Implementing a chaotic lighting control system that uses controllers to vary light characteristics, such as intensity and color, over time based on chaotic functions, which can learn from user interactions and environmental conditions, incorporating elements like machine learning algorithms and network communication for synchronized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional lighting control systems are used to maintain stable lighting conditions, then lighting reliability is improved, but adaptability to user interactions and environmental influences deteriorates
Solution Approach 1:
The lighting control system transitions from static, predetermined control to dynamic control that continuously adapts to changing conditions. The controller modifies lighting parameters in real-time based on chaotic environmental inputs and learned user preferences, enabling the system to respond flexibly to both environmental changes and user interactions while maintaining operational reliability.
Solution Approach 2:
The system implements feedback loops where the controller receives information about actual lighting conditions, user interactions, and environmental factors, then uses this feedback to adjust lighting output. The machine learning component analyzes feedback data to refine control strategies, creating a closed-loop system that improves adaptability while maintaining stability through continuous optimization.
2Adaptability or versatility
If chaotic functions are used to vary light characteristics dynamically, then adaptability is improved, but system complexity increases
Solution Approach 1:
The controller is designed as a multi-functional device that integrates chaotic function generation, machine learning algorithms, environmental sensing, and lighting control capabilities. By consolidating these diverse functions into a single universal controller, the system achieves high adaptability through chaotic dynamics without proportionally increasing overall system complexity, as the controller handles multiple tasks simultaneously.
Solution Approach 2:
The machine learning component enables the system to self-adjust and optimize lighting parameters autonomously based on learned patterns from user interactions and environmental data. This self-service capability reduces the need for complex manual control mechanisms, as the system automatically adapts to changing conditions while maintaining relatively simple hardware architecture.
3Adaptability or versatility
If machine learning algorithms are implemented for learning user interactions, then adaptability is improved, but computational requirements and energy consumption increase
Solution Approach 1:
The machine learning algorithm performs preliminary learning during periods when lighting adjustments are less critical (e.g., nighttime, unoccupied periods), processing data and updating models in advance. This preliminary action allows the system to accumulate learning without always requiring active computational resources during high-priority lighting periods, thereby reducing peak energy consumption while maintaining adaptability.
Solution Approach 2:
The system implements partial machine learning processing by focusing computational resources on the most impactful lighting parameters and time periods rather than continuously optimizing all parameters. The learning algorithm selectively applies computational power to scenarios where adaptability provides the greatest benefit, reducing overall energy consumption while maintaining sufficient adaptability for user interactions.
Data Source
AI summary
At least one controllable source of visible light is configured to illuminate a space to be utilized by one or more occupants. A controller causes the source(s) to emit light in a manner that varies at least one characteristic of visible light emitted into the space over a period of time at least in part in accordance with a chaotic function. Responsive to user input, sensed activity, and/or acquired information, the source(s) are controlled by the controller in accordance with a lighting control function which may be modified based on learning by a device or system including the controller.