Learning Thermostat Power Architecture for Low-Energy HVAC Control
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Solution Overview
Problem
Existing HVAC thermostatic control systems fail to balance energy-saving sophistication with practical, everyday use due to complexity and user intimidation, leading to suboptimal energy usage in homes and buildings.
Innovation Solution
A programmable thermostat with a versatile sensing and control unit (VSCU) that uses high-power and low-power consuming circuitry, power stealing, and a rechargeable battery to optimize energy usage through learning algorithms and user-friendly interfaces, promoting energy-saving behaviors without requiring extensive user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If programmable thermostats with multiple settings and controls are provided, then energy-saving sophistication is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The thermostat performs self-learning of occupancy patterns and temperature preferences automatically without user intervention. The system observes when occupants are present or away and what temperatures they prefer, then autonomously creates and adjusts energy-saving schedules, eliminating the need for complex manual programming while achieving sophisticated energy management
Solution Approach 2:
The system dynamically changes operational parameters based on learned patterns. Instead of requiring users to set fixed parameters, the thermostat continuously adapts temperature setpoints, timing schedules, and HVAC control parameters based on observed occupancy and preference data, providing both simplicity and energy optimization
2Loss of energy
If programmable thermostats with multiple settings and controls are provided, then energy-saving sophistication is improved, but ease of operation worsens
Solution Approach 1:
The thermostat performs self-learning of occupancy patterns and temperature preferences automatically without user intervention. The system observes when occupants are present or away and what temperatures they prefer, then autonomously creates and adjusts energy-saving schedules, eliminating the need for complex manual programming while achieving sophisticated energy management
Solution Approach 2:
The system creates simplified representations of complex patterns by learning and copying occupancy behaviors and temperature preferences. Instead of presenting users with complex scheduling interfaces, the thermostat copies real-world patterns and uses them to automatically control HVAC systems, achieving energy savings through intuitive observation rather than complicated user input
3Measurement precision
If high-power microprocessor is continuously active for learning and wireless communications, then learning precision and network connectivity are improved, but energy consumption increases
Solution Approach 1:
The microprocessor operates in periodic cycles rather than continuously. High-power activities such as learning computations and wireless communications are performed in periodic bursts when sufficient energy is available in the rechargeable battery, while low-power monitoring continues between bursts. This allows precise learning and connectivity while managing overall energy consumption through rhythmic on/off cycles of intensive processing
Solution Approach 2:
The system recovers and stores energy from the HVAC triggering circuitry during HVAC operation into a rechargeable battery. This recovered energy is then discarded from the harvesting circuit and stored for later use by the high-power microprocessor activities. The circuitry that harvests power is temporarily inactive when storing energy, and the battery accumulates energy over time to fuel periodic high-power operations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The VSCU unit effectively optimizes energy usage by automatically adjusting HVAC settings based on occupancy patterns and comfort preferences, encouraging reduced energy consumption while maintaining comfort, and is accessible to both unsophisticated and advanced users.
Implementation Method 1
power stealing circuitry adapted to harvest power from an HVAC triggering circuit for turning on and off an HVAC system function; and a power storage medium, such as a rechargeable battery, adapted to store power harvested by the power stealing circuitry
Data Source
AI summary
A user-friendly, network-connected learning thermostat is described. The thermostat is made up of (1) a wall-mountable backplate that includes a low-power consuming microcontroller used for activities such as polling sensors and switching on and off the HVAC functions, and (2) separable head unit that includes a higher-power consuming microprocessor, color LCD backlit display, user input devices, and wireless communications modules. The thermostat also includes a rechargeable battery and power-stealing circuitry adapted to harvest power from HVAC triggering circuits. By maintaining the microprocessor in a “sleep” state often compared to the lower-power microcontroller, high-power consuming activities, such as learning computations, wireless network communications and interfacing with a user, can be temporarily performed by the microprocessor even though the activities use energy at a greater rate than is available from the power stealing circuitry.