Thermostat Parameter Learning for Adaptive Climate Preference Control
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Solution Overview
Problem
Traditional HVAC systems rely on a 'set and forget' approach to temperature control, which fails to account for various factors influencing indoor comfort, such as humidity and air movement, leading to inefficient climate management.
Innovation Solution
A method and system for relative temperature preference learning that monitors indoor and outdoor conditions to calculate a comfort zone, learns occupant preferences, and adjusts thermostat settings automatically based on historical data and real-time environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional HVAC systems use a 'set and forget' approach to temperature control, then the system operation is simple, but the indoor comfort is poor because the system fails to account for humidity and air movement factors
Solution Approach 1:
The HVAC system automatically monitors multiple environmental parameters (temperature, humidity, air movement) and self-adjusts the climate control settings without requiring user intervention. The system learns occupant preferences over time and autonomously optimizes comfort conditions, eliminating the need for manual temperature setting while maintaining high comfort quality.
Solution Approach 2:
The system continuously monitors indoor environmental conditions including temperature, humidity, and air movement, compares these against comfort thresholds and learned preferences, and automatically adjusts HVAC operation accordingly. This closed-loop feedback mechanism ensures optimal comfort while adapting to changing conditions.
2Reliability
If the system continuously monitors and adjusts temperature settings to learn occupant preferences, then the indoor comfort is improved, but the system complexity increases
Solution Approach 1:
The thermostat device performs multiple functions: it monitors temperature, humidity, and air movement; stores historical environmental data; learns occupant preferences through pattern recognition; and controls HVAC system operation. By consolidating these diverse functions into a single multi-functional device, the patent avoids the need for separate complex systems for each function.
Solution Approach 2:
The system automatically learns occupant temperature preferences by analyzing historical data and behavioral patterns without requiring manual programming or complex user configuration. This self-learning capability reduces the need for complex setup procedures and user interface complexity while maintaining high comfort quality.
3Loss of energy
If the system dynamically adjusts temperature settings based on learned preferences and environmental factors, then energy consumption is reduced, but the loss of information about user intent increases
Solution Approach 1:
The system continuously monitors and records user interactions with the thermostat and manual temperature adjustments, using this feedback to refine its learned preferences. By maintaining an ongoing dialogue with occupants through monitoring and adaptation, the system preserves understanding of user intent while dynamically optimizing energy consumption based on learned patterns.
Solution Approach 2:
The system pre-loads and pre-cools or pre-heats spaces based on learned occupancy patterns and historical preferences before occupants arrive or before temperature changes are typically requested. This anticipatory action reduces energy consumption by avoiding last-minute intensive heating or cooling while maintaining comfort, based on previously gathered information about user behavior.
Data Source
AI summary
A method for relative temperature preference learning is described. In one embodiment, the method includes identifying one or more current settings of a thermostat located at a premises, identifying one or more current indoor and outdoor conditions, calculating a current indoor differential between the current indoor temperature and the current target temperature, calculating a current outdoor differential between the current outdoor temperature and the current target temperature, and learning temperature preferences based on an analysis of the one or more current indoor conditions and the one or more current outdoor conditions. The one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings. The one or more current indoor and outdoor conditions include at least one of a current temperature, current humidity, current indoor airflow, current atmospheric pressure, current level of precipitation, and current cloud cover.