Building Ambient Control Using Occupant Circadian Rhythm Models
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Solution Overview
Problem
Building automation systems (BAS) struggle to account for changes in occupants' physical comfort throughout the day, as personal profiles and preferences do not consider biological cycles that affect physical comfort, leading to suboptimal control of ambient features like heating, ventilation, and lighting.
Innovation Solution
The method generates statistical models of circadian rhythms using data from mobile devices and identifies room occupants to determine tailored ambient settings, incorporating machine learning algorithms to calculate trade-off settings for multiple occupants, thereby improving occupant comfort and productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If building automation systems use personal profiles and preferences to control ambient features, then ease of operation is improved, but adaptability to biological cycles deteriorates
Solution Approach 1:
The system transitions from static personal profiles to dynamic ambient controls that automatically adjust according to real-time circadian rhythm data. The ambient settings are no longer fixed based on user preferences but dynamically adapt to occupants' biological cycles, resolving the contradiction between ease of operation and adaptability to biological changes.
Solution Approach 2:
The system incorporates feedback loops where mobile devices continuously monitor circadian rhythm indicators (such as body temperature, heart rate variability) and feed this data back to the building automation system. This feedback mechanism enables automatic adjustments to ambient features, maintaining ease of operation while achieving adaptability to biological cycles.
2Adaptability or versatility
If building automation systems implement personalized ambient controls for each occupant, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system uses a universal platform that can serve multiple occupants with different circadian rhythms simultaneously. The building automation system integrates with various mobile devices and monitors multiple occupants' data through a unified interface, achieving personalized adaptability without proportionally increasing system complexity.
Solution Approach 2:
The system enables occupants to self-report their presence and the system automatically processes their circadian rhythm data without requiring manual configuration. The ambient controls adjust themselves based on algorithmic processing of circadian data, reducing the operational complexity burden on users while maintaining high adaptability.
3Productivity
If building automation systems monitor and adjust ambient features in real-time based on circadian rhythms, then productivity is improved, but use of energy increases
Solution Approach 1:
The system implements periodic adjustments to ambient features that align with natural circadian rhythms rather than continuous adjustments. Lighting, temperature, and ventilation are modified in periodic cycles that match occupants' biological patterns, achieving productivity benefits while reducing unnecessary energy consumption from constant system adjustments.
Data Source
AI summary
Embodiments are disclosed for a method. The method includes generating statistical models of circadian rhythms based on circadian rhythm data generated by mobile computing devices of occupants of a building having a building automation system. The method also includes identifying room occupants of a room disposed within the building. Additionally, the method includes determining ambient settings for an ambient system operated by the building automation system based on a subset of the statistical models, wherein the subset corresponds to the identified room occupants. The method further includes determining a trade-off ambient setting based on the ambient settings.