Multi-Zone HVAC Control Coupling Detection and Delay Adjustment
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Solution Overview
Problem
Existing HVAC control systems face challenges in energy efficiency due to user intimidation by complex controls and lack of automatic energy-saving features, leading to suboptimal operation in residential and commercial settings.
Innovation Solution
A programmable thermostat with high-power and low-power consuming circuitry, including a microprocessor and microcontroller, that interfaces with users, polls sensors, and harvests power to optimize HVAC operation, featuring a user-friendly interface and advanced energy-saving algorithms that learn user habits and adjust settings automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If programmable thermostats with multiple settings and controls are provided, then energy-saving capability is improved, but device complexity increases making users intimidated and unable to use the features
Solution Approach 1:
The thermostat system performs self-learning by automatically monitoring user manual adjustments and environmental conditions to generate optimized temperature schedules without user intervention. The system serves itself by converting manual user interactions into automated programming data, eliminating the need for users to program complex schedules while still achieving energy-saving goals.
Solution Approach 2:
The system continuously monitors user manual temperature adjustments and uses this feedback to refine its learned schedules. By detecting when users manually override the thermostat and incorporating this behavioral data into its learning algorithm, the system adapts to actual usage patterns and improves energy-saving performance over time.
2Ease of operation
If simple non-programmable thermostats are used, then ease of operation is improved, but energy-saving opportunities are lost due to lack of automatic control
Solution Approach 1:
The thermostat automatically learns and programs temperature schedules by monitoring user behavior patterns from manual adjustments. This self-programming capability transforms a simple-to-operate device into an energy-saving system without requiring users to understand or program complex settings.
Solution Approach 2:
The system proactively adjusts temperatures in advance of predicted user needs by learning from historical manual adjustment patterns. This preliminary action allows the thermostat to pre-condition spaces before occupants arrive or wake, achieving energy savings without requiring users to plan or program schedules ahead of time.
3Ease of operation
If manufacturer default profiles are used in programmable thermostats, then ease of operation is improved, but energy-saving effectiveness is reduced due to one-size-fits-all approach
Solution Approach 1:
The thermostat automatically customizes temperature schedules by learning individual user behavior patterns through monitored manual adjustments. This self-customization eliminates the one-size-fits-all problem by adapting the control profile to each household's specific routines and preferences without requiring manual programming.
Solution Approach 2:
The system dynamically adapts its control strategy by continuously learning from evolving user behaviors and environmental conditions. Rather than using static manufacturer defaults, the thermostat evolves its programming over time to match changing household patterns, making the system adaptable to diverse and changing user needs.
Data Source
AI summary
The current application is related to environmental-conditioning systems controlled by intelligent controllers and, in particular, to an intelligent-thermostat-controlled HVAC system that detects and ameliorates control coupling between intelligent thermostats. Control coupling can lead to inefficient HVAC operation. When control coupling is detected, a settings-adjustment directive is sent to at least one intelligent thermostat to adjust one or more intelligent-thermostat settings, including an HVAC-cycle-initiation delay parameter, swing parameter, and a parameter that indicates whether or not an intelligent thermostat should first obtain confirmation or permission before initiating an HVAC cycle.