Computer Vision Lighting Control for Large Hybrid Workspaces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to efficiently manage energy consumption and reduce carbon emissions in large open workspaces due to the difficulty in automatically controlling overhead lighting, especially in hybrid work models where employee presence is unpredictable.
Innovation Solution
A computer vision-based system using cameras and AI algorithms to monitor light usage and employee presence, detecting ceiling planes and floor planes, identifying light fixtures and individuals, and determining energy and carbon emission patterns to automate lighting control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If motion sensors are used to automatically control lighting, then lighting control automation is improved, but the system only works effectively in small rooms and cannot handle large open workspaces
Solution Approach 1:
The patent replaces motion sensors with computer vision technology using cameras and AI algorithms. The system uses image processing to detect employee presence, identify ceiling planes and floor planes, and determine lighting control decisions, thereby substituting the mechanical motion sensing approach with a computational vision-based approach that scales better to large spaces.
Solution Approach 2:
The patent transitions from point-based motion detection to area-based image analysis. By using cameras to capture 2D images of the entire workspace and processing them to identify presence across different zones (ceiling planes, floor planes), the system achieves comprehensive coverage of large open workspaces that motion sensors cannot handle.
2Loss of energy
If manual lighting control is used in hybrid work models, then energy consumption can be reduced, but facility management personnel face difficulty in predicting and controlling lighting based on unpredictable employee presence
Solution Approach 1:
The system enables self-service lighting control by automatically detecting employee presence through computer vision and making intelligent decisions about lighting control without requiring facility management personnel intervention. The AI algorithms analyze images, predict presence patterns, and autonomously adjust lighting, freeing personnel from manual control tasks.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors employee presence using cameras, processes this information through AI algorithms, and adjusts lighting accordingly. The system learns from observed patterns and refines its predictions, creating a closed-loop control system that adapts to unpredictable work patterns.
3Illumination intensity
If overhead lighting is used throughout large open workspaces, then uniform lighting coverage is achieved, but energy consumption increases due to inability to automatically control lighting in such spaces
Solution Approach 1:
The patent applies local quality control by detecting the specific locations of employees and adjusting lighting only in those areas rather than uniformly across the entire workspace. The system identifies ceiling planes and floor planes to determine precise lighting zones, allowing localized lighting control that maintains uniformity where needed while reducing energy consumption in unoccupied areas.
Data Source
AI summary
A system comprises at least one camera disposed in an environment, and at least one computing node which hosts and executes an energy management system. The energy management system is configured to utilize computer vision processing of the images of the environment to extract information from the images, to utilize the extracted information from the images to determine usage patterns of light sources in the environment, and to generate intelligent recommendations for automated control of the light sources in the environment based on the determined usage patterns, to conserve energy consumption from use of the light sources in the environment.