Monitoring system for residential HVAC systems
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Solution Overview
Problem
Current residential HVAC monitoring systems lack capabilities to enhance performance, save energy, and reduce costs, as they are not equipped with advanced features like real-time fault detection, energy usage alerts, and 'what-if' scenario analysis for optimal decision-making.
Innovation Solution
A smart system utilizing wirelessly connected sensors, a home thermal model based on heat transfer principles, a performance monitoring and fault detection algorithm, and a predictive algorithm for temperature and energy-cost estimation, enabling real-time monitoring, fault detection, and cost-saving decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If smart thermostats are made remotely accessible through the Internet, then user convenience and monitoring capability are improved, but device complexity and cost increase
Solution Approach 1:
The system divides functionality between the smart thermostat (local control unit) and remote devices (smartphone, tablet, computer). The thermostat handles local sensing and control, while remote devices provide user interface and data visualization, reducing the complexity burden on any single component.
Solution Approach 2:
A wireless communication module serves as an intermediary between the thermostat and remote devices, enabling Internet connectivity without requiring complex embedded systems in the thermostat itself. This mediator handles data transmission and protocol conversion.
2Reliability
If performance monitoring and fault detection capabilities are added to HVAC systems, then system reliability and energy efficiency are improved, but device complexity and initial cost increase
Solution Approach 1:
The system continuously monitors HVAC performance parameters (temperature, humidity, energy consumption) and provides feedback through alerts and notifications when anomalies are detected. This enables proactive fault detection and maintenance scheduling without requiring complex diagnostic hardware.
Solution Approach 2:
Traditional mechanical fault detection methods are replaced with electronic sensing and software-based analysis. Sensors monitor system parameters, and algorithms detect patterns indicating faults, replacing the need for manual inspection and complex mechanical diagnostic devices.
3Loss of energy
If energy usage monitoring and predictive algorithms are implemented, then energy cost savings are improved, but loss of information and data processing requirements increase
Solution Approach 1:
The system performs preliminary data processing and analysis locally at the thermostat, pre-calculating energy consumption patterns and predicting future usage. This reduces the need for extensive cloud-based data processing and minimizes information loss during transmission.
Solution Approach 2:
The system transforms raw sensor data into meaningful performance metrics and energy consumption parameters. By changing data representation from raw measurements to processed indicators, the system reduces information complexity while preserving essential energy management insights.
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 system reduces energy costs by 10% for HVAC systems, resulting in annual savings of $135 to $162, with a payback period of approximately two years, by providing accurate performance monitoring and predictive capabilities.
Implementation Method 1
a home thermal model based on heat transfer principles
Data Source
AI summary
A system and method for controlling HVAC equipment in a residential setting. The system may include an outdoor temperature sensor positioned to measure outdoor temperatures, an indoor air temperature sensor to measure indoor space air temperatures, a supply duct air temperature sensor positioned to measure supply duct air temperatures, a return duct air temperature sensor positioned to measure return duct air temperatures, an air blower current sensor positioned to measure air blower currents, and/or an air compressor current sensor positioned to measure air compressor currents, and a controller operable to receive the measures of outdoor temperature, indoor space air temperature, supply duct air temperature, return duct air temperature, air blower current, air compressor current, and measures of solar irradiation intensity and wind speed. The controller may be programmed with instructions to input the measures into a thermal model for outputting signals for implementing changes in the system.