HVAC Temperature Sequence Control for Comfort-Energy Balance
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Solution Overview
Problem
Smart thermostats often have complex interfaces that users find difficult to interact with, leading to underutilization of their capabilities, as users either leave the existing schedule unchanged or override it, resulting in suboptimal energy efficiency and comfort.
Innovation Solution
A processor-implemented method and system that transforms comfort map data into sequential constant-temperature segments to generate a control temperature sequence, balancing user comfort and energy efficiency by utilizing direct user feedback and thermal equilibrium boundaries, optimizing the HVAC system's operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If smart thermostats use complex interfaces to provide advanced control capabilities, then the system functionality is improved, but user interaction difficulty increases leading to underutilization
Solution Approach 1:
The thermostat system automatically learns and adapts to user temperature preferences and occupancy patterns without requiring manual programming. The system performs self-configuration by monitoring user interactions and environmental data, generating optimized temperature schedules autonomously, thereby eliminating the need for complex user interfaces while maintaining advanced functionality
Solution Approach 2:
The system continuously monitors user manual adjustments to temperature settings and uses this feedback to refine its learned model of user preferences. By incorporating real-time feedback from occupancy sensors and temperature adjustments, the thermostat adapts its control strategy dynamically, providing intelligent automation without requiring complex user interaction
2Ease of operation
If users leave the existing thermostat schedule unchanged due to complexity, then ease of operation is maintained, but energy efficiency deteriorates
Solution Approach 1:
The thermostat automatically generates and optimizes energy-saving schedules without user intervention. By continuously learning from occupancy patterns, weather forecasts, and user preferences, the system autonomously adjusts temperature setpoints to minimize energy consumption while maintaining comfort, eliminating the energy waste associated with static default schedules
Solution Approach 2:
The system pre-cools or pre-heats spaces before anticipated occupancy based on learned patterns and calendar data, and pre-conditioning spaces after occupancy ends. This preliminary action optimizes energy usage by avoiding unnecessary heating or cooling during unoccupied periods while ensuring comfort is ready when needed, achieving energy efficiency without requiring users to program schedules
3Loss of energy
If smart thermostats provide advanced scheduling capabilities, then energy savings potential is improved, but device complexity increases
Solution Approach 1:
The thermostat performs automatic self-configuration and optimization without requiring users to navigate complex menus or programming interfaces. The system autonomously learns occupancy patterns, preferences, and environmental factors to generate optimized schedules, providing advanced energy-saving functionality through invisible automation rather than complex user interfaces
Solution Approach 2:
The system uses machine learning algorithms and environmental sensors as intermediaries between user needs and HVAC control. Rather than requiring direct user programming, the intermediary learning model translates occupancy patterns and preferences into optimized temperature schedules, simplifying the user experience while maintaining sophisticated energy management capabilities
Data Source
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AI summary
The apparatuses, methods and systems for comfort and energy efficicency conformance in an HVAC system transforms a comfort map into a plurality of sequential constant-temperature segments that are used in generating a control temperature sequence that preserves occupant comfort while improving energy efficiency.