Predictive Daylight Harvesting With Feedback and Self-Tuning Control
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Solution Overview
Problem
Current daylight harvesting systems are inefficient due to the lack of suitable design tools for simulating performance, often operating in open loop mode without feedback, and fail to maximize energy savings while maintaining occupant comfort, as they are designed based on limited information and lack the ability to adjust parameters for optimal performance.
Innovation Solution
A predictive daylight harvesting system that uses a controller to read various sensor inputs, analyze historical and current weather data, and occupant behavior, and adjust lighting and fenestration devices to maximize energy savings through an interactive simulation and self-tuning mechanism, allowing for hour-by-hour simulation of energy savings over a year and autonomous optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional daylight harvesting systems operate in open loop mode without feedback, then the system is simpler to implement, but energy savings are not maximized and performance cannot be optimized
Solution Approach 1:
The patent implements a closed-loop control system that continuously monitors actual daylight levels, occupancy patterns, and energy consumption, then uses this feedback to dynamically adjust lighting and fenestration settings. This feedback mechanism enables the system to learn from historical data and optimize performance over time, resolving the contradiction by accepting increased system complexity to achieve maximum energy savings.
Solution Approach 2:
The system performs preliminary simulations and calculations during the design phase to predict optimal daylight harvesting strategies. By pre-calculating form factors, daylight coefficients, and energy savings potential for various design parameters, the system establishes an optimized baseline before actual operation begins, enabling faster real-time control while maintaining energy efficiency.
2Adaptability or versatility
If design parameters are fixed during construction, then the building can be constructed more quickly, but the system cannot adapt to optimize energy savings under varying weather and occupancy conditions
Solution Approach 1:
The patent transforms fixed design parameters into dynamic, adjustable settings that can be modified in real-time based on weather forecasts, actual daylight measurements, and occupancy patterns. The system continuously recalculates optimal lighting and fenestration configurations, allowing the building to adapt its daylight harvesting strategy to varying conditions while maintaining a straightforward construction process with standard fixed installations.
Solution Approach 2:
The system enables post-construction modification of design parameters such as lighting levels, blind positions, and window treatments without requiring physical reconstruction. By allowing digital adjustment of operational parameters rather than physical parameters, the system achieves adaptability to varying conditions while maintaining construction simplicity.
3Loss of energy
If comprehensive simulations are performed for all design parameters, then optimal energy savings can be achieved, but the computational time and resources required increase significantly
Solution Approach 1:
The patent performs comprehensive simulations and calculations during the design phase to pre-determine optimal strategies for various weather and occupancy scenarios. By calculating form factors, daylight coefficients, and energy savings potential in advance, the system creates a lookup table of optimized settings that can be quickly retrieved and applied during real-time operation, avoiding the need for time-consuming calculations during actual control operations.
Solution Approach 2:
The system performs full comprehensive simulations only when necessary, such as during initial design optimization or when significant performance deviations are detected. For routine operations, it uses simplified models and pre-calculated data, performing partial simulations that focus only on the most critical parameters, thus achieving adequate energy savings without excessive computational overhead.
Data Source
AI summary
In the context of a predictive daylight harvesting system data values are input regarding a plurality of variable building design parameters. The effects on a building's environmental characteristics are calculated based on the data values regarding a plurality of building design parameters. At least one of the data values is changed regarding variable building design parameters. The effects on a building's environmental characteristics are recalculated based on the data values regarding a plurality of building design parameters building heat balance.