HVAC Comfort Map Segmentation for Self-Learning Energy Efficiency
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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 thermostat schedule unchanged or override it, resulting in suboptimal energy efficiency and comfort.
Innovation Solution
A system that transforms comfort map data into sequential constant-temperature segments to generate a control temperature sequence, balancing user comfort, temperature trajectory complexity, and energy efficiency by utilizing comfort map metric data and direct user feedback, optimizing HVAC system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a smart thermostat with advanced capabilities is provided, then energy efficiency and comfort control potential is improved, but interface complexity increases making it difficult for users to interact with
Solution Approach 1:
The thermostat system performs self-learning by automatically monitoring user temperature adjustments and occupancy patterns over time, building customized temperature schedules without requiring user programming or complex interactions. The system serves itself by autonomously optimizing energy efficiency based on observed behavior patterns.
Solution Approach 2:
The system continuously monitors user manual adjustments to temperature settings and uses this feedback to refine and adapt the learned schedules. By incorporating real-time user feedback loops, the thermostat improves its energy efficiency performance while maintaining simple operation, as users only need to make natural adjustments without navigating complex interfaces.
2Productivity
If users are required to program detailed temperature schedules, then energy efficiency optimization is improved, but ease of operation deteriorates as users leave schedules unchanged or override them
Solution Approach 1:
The thermostat autonomously observes when users manually adjust temperatures and automatically constructs optimized schedules from these observations, eliminating the need for users to perform the complex task of programming schedules. The system learns occupancy patterns and temperature preferences, generating energy-efficient schedules that adapt automatically to changing user needs.
Solution Approach 2:
The system performs preliminary learning during an initial period by monitoring user behavior patterns and pre-building customized temperature schedules before full operation begins. This preliminary action establishes a foundation of optimized schedules that are already tailored to user preferences, eliminating the need for users to manually program schedules later.
3Productivity
If complex temperature trajectories are implemented, then energy efficiency is improved, but stress on HVAC components increases reducing their operational life
Solution Approach 1:
The learned temperature schedules are divided into discrete constant-temperature segments rather than continuous complex trajectories. By segmenting the temperature profile into stepped levels, the system achieves energy efficiency through strategic temperature maintenance while reducing the frequency and magnitude of rapid temperature changes that stress HVAC components.
Solution Approach 2:
The system dynamically adjusts the number and duration of temperature segments based on learned occupancy patterns and environmental conditions. During periods of high occupancy or extreme outdoor temperatures, the system implements more frequent segments to maintain comfort, while during transitional periods it uses fewer, longer segments to reduce HVAC cycling and component stress, optimizing the balance between energy efficiency and reliability.
Data Source
AI summary
A processor-implemented method of controlling 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. The method balances user comfort, temperature trajectory execution complexity, operational HVAC trajectory realization, and energy efficiency. The system achieves this balance through utilization of comfort map metric data, analysis and control system execution. The system, comfort map metric data processing and analysis facilitates this balance by executing a control temperature sequence/temperature trajectories developed based on direct user comfort feedback data within the context of a comfort map and thermal equilibrium boundaries derived from the comfort map.