Operational constraint optimization apparatuses, methods and systems
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Solution Overview
Problem
Current HVAC systems lack the ability to learn heat transfer characteristics and occupant comfort preferences autonomously, leading to inefficient energy consumption and comfort issues in residential and commercial buildings.
Innovation Solution
A processor-implemented method and system that utilizes a comfort agent to predict energy consumption and temperature by learning from weather estimations and thermal properties, optimizing temperature control through a multi-dimensional space analysis, minimizing energy consumption while maintaining occupant comfort without requiring commissioning information or significant occupant interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional HVAC systems operate without learning capabilities, then the system structure remains simple, but energy consumption increases and comfort optimization is lost
Solution Approach 1:
The HVAC system performs self-learning of heat transfer characteristics and occupant comfort preferences autonomously without external commissioning or significant user interaction. The comfort agent continuously learns from weather data, thermal properties, and occupancy patterns to optimize temperature control, enabling the system to improve its own energy efficiency automatically.
Solution Approach 2:
The system implements continuous feedback loops where the comfort agent monitors thermal responses, weather conditions, and occupancy patterns to dynamically adjust temperature setpoints. This feedback mechanism enables the system to learn from past performance and continuously improve energy consumption while maintaining comfort.
2Reliability
If the system requires commissioning information and significant occupant interaction to learn preferences, then comfort optimization improves, but ease of operation deteriorates
Solution Approach 1:
The comfort agent autonomously learns occupant comfort preferences and heat transfer characteristics without requiring commissioning information or significant user interaction. The system self-calibrates by observing thermal responses and occupancy patterns, eliminating the need for manual setup while maintaining reliable comfort optimization.
Solution Approach 2:
The comfort agent acts as an intermediary that translates weather data, thermal properties, and occupancy patterns into optimized temperature control decisions. This intermediary layer enables the system to learn and adapt without direct user involvement, bridging the gap between environmental data and comfort optimization.
3Productivity
If the system uses multi-dimensional space analysis to optimize temperature control, then energy efficiency improves, but device complexity increases
Solution Approach 1:
The system employs multi-dimensional space analysis by incorporating weather conditions, thermal properties, occupancy patterns, and time variables into the optimization process. This dimensional expansion enables comprehensive energy efficiency optimization while the comfort agent learns the relationships between these dimensions through continuous observation and adaptation.
Data Source
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.