Predictive Daylight Harvesting With Feedback Lighting Control
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Solution Overview
Problem
Current daylight harvesting systems are inefficient due to reliance on basic rules of thumb for design, lack of suitable simulation tools for complex systems, and often operate in open loop mode without feedback, resulting in suboptimal energy savings and occupant comfort.
Innovation Solution
A predictive daylight harvesting system that uses a combination of sensors, mathematical models, and artificial intelligence to simulate and optimize daylight usage based on historical data, current weather conditions, and occupant behavior, allowing for real-time adjustments to electric lighting and fenestration to maximize energy savings while maintaining comfortable environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If basic rules of thumb are used for daylight harvesting system design, then the system is simple to implement, but energy savings are suboptimal
Solution Approach 1:
The system performs preliminary simulation and prediction of daylight harvesting performance before actual implementation. By using historical weather data and mathematical models to predict future daylight conditions, the system optimizes control strategies in advance, achieving energy savings without complex real-time adjustments.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual daylight conditions, photosensor readings, and energy consumption. This feedback is used to refine predictions and adjust control strategies, progressively improving energy savings while maintaining system simplicity.
2Ease of operation
If open loop mode operation is used, then the system is easy to operate, but occupant comfort is compromised
Solution Approach 1:
The system automatically adjusts lighting and fenestration controls based on predicted daylight conditions without requiring manual intervention. It self-regulates to maintain optimal illuminance levels, providing both ease of operation and reliable occupant comfort through autonomous decision-making.
Solution Approach 2:
The system uses feedback from photosensors and occupancy detection to automatically adjust lighting levels, ensuring comfort is maintained without user intervention. The closed-loop control responds to actual conditions while maintaining simple operation.
3Productivity
If complex simulation tools are developed, then system optimization is improved, but device complexity increases
Solution Approach 1:
The system introduces a predictive simulation model as an intermediary between simple control rules and complex system behavior. This mathematical model layer translates basic inputs into optimized control strategies, achieving high productivity without requiring the full complexity of detailed simulation tools.
Solution Approach 2:
The system optimizes performance by dynamically adjusting control parameters based on predicted conditions rather than changing the fundamental system architecture. This allows sophisticated optimization while maintaining relatively simple device complexity through parameter-based control.
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 interactive simulation and self-tuning of daylight harvesting systems to achieve annual energy savings of 40-50% while ensuring occupant comfort and optimal plant growth conditions, by accurately predicting and responding to solar insolation and occupant behavior.
Implementation Method 1
Measurement data from one or more photosensors is obtained that provides measurements of an initial distribution of direct and interreflected radiation within the environment
Data Source
AI summary
In an example, an expected sky condition is calculated for a geographic location, a time of day, and a date based on a mathematical model. A predicted distribution of direct and interreflected solar radiation within the environment is calculated based on the expected sky condition. Measurement data from one or more photosensors is obtained that provides measurements of an initial distribution of direct and interreflected radiation within the environment, including radiation from solar and electrical lighting sources. A target distribution of direct and interreflected artificial electromagnetic radiation produced by electrical lighting is determined, based on the measurement data and the predicted distribution of direct and interreflected solar radiation, to achieve the target distribution of direct and interreflected radiation within the environment. Output parameters are set to one or more devices to modify the initial distribution to achieve the target distribution of direct and interreflected radiation within the environment.