Adaptive Thermal Comfort Control Without Building Commissioning
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Solution Overview
Problem
Current thermal management systems for buildings lack efficiency in adapting to environmental changes and occupant preferences, leading to suboptimal energy consumption and comfort levels, as they require commissioning information and frequent occupant interaction.
Innovation Solution
A processor-implemented method and system for adaptive temperature control using a comfort agent that learns heat transfer characteristics and occupant comfort preferences, optimizing energy consumption by calculating control temperature vectors based on thermal device properties, weather estimations, and constraint properties to regulate temperature in thermal systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional thermal management systems use fixed temperature settings and require commissioning information, then system complexity is reduced, but adaptability to environmental changes and occupant preferences deteriorates
Solution Approach 1:
The comfort agent autonomously learns heat transfer characteristics and occupant preferences without requiring commissioning information or manual input. The system performs self-calibration by observing environmental data and thermal responses, eliminating the need for complex setup procedures while maintaining high adaptability to changing conditions
Solution Approach 2:
The patent replaces traditional mechanical control systems with a data-driven comfort agent that uses machine learning algorithms to predict thermal behavior. This substitution enables the system to adapt to environmental changes through computational models rather than complex mechanical adjustments, resolving the contradiction between adaptability and system complexity
2Measurement precision
If traditional thermal management systems require frequent occupant interaction, then measurement precision of comfort preferences is improved, but ease of operation deteriorates
Solution Approach 1:
The comfort agent continuously monitors environmental conditions and thermal responses, using feedback loops to refine its understanding of occupant preferences over time. This automated feedback mechanism achieves high measurement precision without requiring frequent manual input from occupants, thereby maintaining ease of operation
Solution Approach 2:
The system performs self-calibration by automatically learning occupant comfort preferences through observation of thermal responses and environmental data. This eliminates the need for frequent occupant interaction while maintaining precise measurement of comfort preferences, resolving the contradiction between measurement precision and ease of operation
3Use of energy by moving object
If thermal management systems use simplified control methods, then ease of manufacture is improved, but energy consumption optimization deteriorates
Solution Approach 1:
The comfort agent performs preliminary learning and calibration actions to build accurate thermal models of the environment and occupant preferences. By pre-processing environmental data and thermal characteristics, the system enables more efficient energy optimization without requiring complex real-time control mechanisms, resolving the contradiction between energy consumption and control system complexity
Data Source
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AI summary
A system for comfort based management of thermal systems, including residential and commercial buildings with active cooling and/or heating, is described. The system can operate without commissioning information, and with minimal occupant interactions, and can learn heat transfer and thermal comfort characteristics of the thermal systems so as to control the temperature thereof while minimizing energy consumption and maintaining comfort.