Network Thermostat Schedule Learning for Easier HVAC Programming
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Solution Overview
Problem
Existing thermostats are often intimidating for users due to complex controls, leading to reduced user satisfaction and energy-saving opportunities, as users tend to resort to default programs rather than customizing settings for optimal energy efficiency and comfort.
Innovation Solution
A versatile sensing and control unit (VSCU) with a user-friendly interface, featuring a rotatable ring for easy navigation and selection, allows users to set temperature preferences and schedules, while incorporating automated learning to optimize energy usage based on occupancy patterns and comfort preferences, and connects to social networking for shared schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If complex controls are provided on the thermostat, then energy-saving opportunities increase, but user satisfaction decreases
Solution Approach 1:
The thermostat automatically learns user temperature preferences and occupancy patterns through sensors and user interactions, then generates HVAC schedules autonomously without requiring manual programming. This self-learning capability allows the system to capture energy-saving opportunities while eliminating the complexity of manual control configuration.
Solution Approach 2:
The system dynamically adjusts temperature setpoints based on learned user preferences and real-time conditions, automatically optimizing energy savings without requiring users to understand or configure complex parameters. The thermostat adapts its control strategy by changing temperature parameters according to learned patterns.
2Loss of energy
If automated learning is implemented, then energy optimization improves, but device complexity increases
Solution Approach 1:
The patent replaces manual mechanical programming interfaces with automated electronic learning systems that use sensors and processors to observe user behavior patterns. This substitution eliminates the need for complex user-facing controls while implementing sophisticated energy optimization through automated algorithms.
Solution Approach 2:
The system introduces sensors and automated processing as intermediaries between the user and the HVAC control system. These intermediaries capture user preferences and environmental data, then translate them into optimized scheduling decisions, shielding users from the underlying complexity while achieving energy optimization.
3Ease of operation
If default programs are used, then ease of operation improves, but energy-saving opportunities are reduced
Solution Approach 1:
The thermostat performs preliminary automatic learning of user preferences and occupancy patterns during an initial period, then uses this learned information to generate customized energy-saving schedules. This preliminary automated action replaces default programs with personalized schedules that capture energy-saving opportunities while requiring minimal user input.
Data Source
AI summary
HVAC schedules may be programmed for a thermostat using a combination of pre-existing schedules or templates and automated schedule learning. For example, a pre-existing schedule may be initiated on the thermostat and the automated schedule learning may be used to update the pre-existing schedule based on users' interactions with the thermostat. The preexisting HVAC schedules may be stored on a device or received from a social networking service or another online service that includes shared HVAC schedules.