HVAC Efficiency Evaluation Using Weather and Thermal Mass Feedback
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Solution Overview
Problem
Conventional thermostats lack the ability to account for external weather conditions, thermal mass of buildings, and dynamic factors affecting HVAC system efficiency, leading to sub-optimal energy usage and increased energy bills.
Innovation Solution
A system that uses remote temperature sensors, processors, and databases to calculate thermal mass and operational efficiency, diagnose issues, and adjust settings based on outside weather data, occupancy, and HVAC system performance, connected through a network for real-time monitoring and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional thermostats are used with limited user interface, then device complexity is reduced and cost is lowered, but energy savings are rarely realized and user programming is abandoned
Solution Approach 1:
The thermostat system performs self-learning by automatically observing user temperature adjustments and occupancy patterns over time, building customized temperature schedules without requiring manual programming. This eliminates the need for complex user interfaces while achieving optimal energy savings through autonomous adaptation to household routines.
Solution Approach 2:
The system continuously monitors user manual adjustments to temperature settings and uses this feedback to refine and update its learned schedules. By incorporating real-time feedback from occupancy sensors and user interactions, the thermostat adapts its control strategy to maintain comfort while maximizing energy savings, resolving the contradiction between simplicity and effectiveness.
2Use of energy by moving object
If programmable thermostats are used with multiple temperature settings, then theoretical energy savings increase, but ease of operation decreases due to complex programming requirements
Solution Approach 1:
The thermostat automatically generates optimized temperature schedules by learning from observed user behavior patterns, eliminating the need for manual programming of multiple temperature settings. The system autonomously determines when to heat or cool based on learned occupancy patterns, maintaining theoretical energy savings while dramatically simplifying operation.
Solution Approach 2:
The system pre-cools or pre-heats spaces based on learned occupancy patterns before users arrive home or wake up, achieving energy savings through anticipatory control without requiring users to program schedules in advance. This preliminary action is automatically determined through continuous learning of household routines.
3Device complexity
If thermostats only use two input signals (ambient temperature and preset desired temperature), then device complexity is minimized, but adaptability to external weather conditions and building characteristics is limited
Solution Approach 1:
The system continuously monitors the relationship between outdoor temperature, indoor temperature response, and HVAC cycling patterns to learn building-specific thermal characteristics. This feedback loop enables the thermostat to adapt to external weather conditions and building thermal mass without adding complex sensors, maintaining simplicity while improving adaptability.
Solution Approach 2:
The thermostat uses outdoor temperature data as an intermediary signal to predict indoor temperature trends and pre-adjust heating or cooling accordingly. By incorporating weather forecast data and learned building response characteristics, the system anticipates temperature changes and adjusts HVAC operation proactively, enhancing adaptability without direct measurement of all environmental factors.
4Ease of operation
If thermostats are located in sub-optimal positions (direct sunlight, far from ductwork), then installation flexibility increases, but measurement precision of ambient temperature deteriorates
Solution Approach 1:
The system detects temperature discrepancies by comparing thermostat readings with outdoor temperature data and learned building thermal patterns. When solar heating or other local factors cause inaccurate readings, the feedback mechanism identifies the deviation and adjusts the control strategy to compensate, maintaining measurement precision despite sub-optimal thermostat placement.
Solution Approach 2:
The thermostat uses outdoor temperature and building thermal mass characteristics as intermediary references to infer the actual ambient temperature when direct sensing is compromised. By comparing expected temperature behavior based on weather data with actual readings, the system identifies and corrects for localized heating or cooling effects at the thermostat location.
Data Source
AI summary
The invention comprises systems and methods for evaluating changes in the operational efficiency of an HVAC system over time. The climate control system obtains temperature measurements from at least a first location conditioned by the climate system, and status of said HVAC system. One or more processors receives measurements of outside temperatures from at least one source other than said HVAC system and compares said temperature measurements from said first location with expected temperature measurements. The expected temperature measurements are based at least in part upon past temperature measurements.