Daylight Sensing With View-Matrix Modeling for Sparse Sensor Layouts
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Solution Overview
Problem
Current daylight sensing systems in buildings are expensive to install and require numerous sensors due to limited sampling areas, which are intrusive and difficult to redeploy without recommissioning.
Innovation Solution
A system using a luminance sensor with a hemispherical core and multiple light-sensing elements arranged in a Klems basis pattern to measure luminance distribution, combined with a data processor and control system for real-time daylight evaluation, allowing deployment near glazing units and calculating indoor daylight distribution using precomputed view matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense arrays of luxmeters or cameras are used to monitor daylight environment, then measurement precision is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent introduces a computational model as an intermediary between the simple luminance sensor and the desired comprehensive daylight distribution data. The model uses precalculated view matrices to transform limited sensor measurements into full spatial daylight distribution, eliminating the need for dense sensor arrays while maintaining measurement precision
Solution Approach 2:
The system creates a virtual copy of the physical space through computational modeling. Instead of physically placing sensors throughout the space, the view matrix model generates a digital representation of daylight distribution that can be queried at any location, replacing the need for physical sensor copies throughout the environment
2Measurement precision
If luxmeters are placed on workbenches or ceilings to measure local daylight, then measurement precision at specific points is improved, but ease of operation and redeployment deteriorate
Solution Approach 1:
The single luminance sensor combined with the computational model serves multiple functions simultaneously - it can measure daylight distribution for any location in the space without repositioning. The view matrix model allows the system to provide daylight data for workbenches, ceilings, walls, or any other location by simple software queries, making the system universally applicable throughout the entire space
3Area of stationary object
If many sensors are deployed to measure daylight in large office spaces, then coverage area is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent transitions from a spatial distribution of sensors to a computational dimension. Instead of covering the space physically with multiple sensors, the view matrix model uses precalculated geometric relationships to provide infinite spatial coverage through computational queries, adding a mathematical dimension to the measurement system
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time, high-definition daylight evaluation across large areas with minimal sensors, reducing installation costs and intrusion, and facilitating easy redeployment, while providing accurate lighting control.
Implementation Method 1
a light sensor configured to be arranged to detect light entering a sub-volume of the space of interest to provide measurement data corresponding to at least an intensity and direction distribution of light
Implementation Method 2
an optical core having a concave surface and arranged behind the front panel so that light that passes through the aperture would impinge upon the concave surface
Data Source
AI summary
A system for determining light conditions in a space of interest and to control a device thereon includes a light sensor arranged to detect light entering a sub-volume of the space of interest to provide measurement data corresponding to at least an intensity and direction distribution of light entering the sub-volume: a data processor configured to communicate with the light sensor to receive the measurement data, the data processor configured with a computational model to provide a calculated distribution of light based on the measurement date from the sub-volume of the space of interest; and a control system configured to communicate with the data processor to receive the calculated distribution of light and provide control signals based on the calculated distribution of light.