HVAC system detecting user discomfort
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Solution Overview
Problem
HVAC systems lack the ability to accurately determine user discomfort, as they only monitor temperature setpoints without considering individual user preferences and interactions, leading to potential discomfort and inefficiencies.
Innovation Solution
A controller system that receives data on user interactions with the HVAC system, calculates a discomfort score based on temperature differences and user interactions, and generates alerts when discomfort thresholds are exceeded, allowing for proactive adjustments and resource conservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the HVAC system only monitors temperature setpoints without tracking user interactions, then the system operation is simple and energy consumption is low, but the system cannot accurately determine user discomfort
Solution Approach 1:
The system implements feedback by continuously monitoring user interactions with the thermostat and mobile device, then using this feedback to update discomfort scores and trigger alerts when discomfort thresholds are exceeded. This closed-loop approach enables accurate discomfort detection without requiring complex additional hardware.
Solution Approach 2:
The HVAC system uses its existing communication infrastructure and control interfaces to self-monitor user behavior patterns. By analyzing interactions with the thermostat and mobile device, the system automatically determines user discomfort without requiring external monitoring systems or additional sensors.
2Measurement precision
If the system accumulates and analyzes user interaction data to determine discomfort, then user comfort monitoring improves, but data processing requirements and system resource usage increase
Solution Approach 1:
The system applies partial action by only performing full discomfort analysis when temperature differences exceed a first threshold. During normal operating conditions, the system uses simpler monitoring, reducing processor energy consumption while maintaining adequate discomfort detection capability when needed.
Solution Approach 2:
The system pre-establishes discomfort thresholds and alert criteria before operation begins. By having these parameters predetermined, the system avoids complex real-time calculations and decision-making, reducing processor energy consumption during operation while maintaining accurate discomfort detection.
3Reliability
If the system generates alerts when discomfort thresholds are exceeded, then proactive maintenance is enabled, but false alerts may cause user annoyance and system intervention
Solution Approach 1:
The system pre-establishes discomfort thresholds based on accumulated user interaction data before generating alerts. By using historically learned behavior patterns to set personalized thresholds, the system reduces false alerts while maintaining reliable detection of genuine discomfort conditions.
Solution Approach 2:
The alert thresholds are dynamic rather than fixed, adapting to individual user behavior patterns over time. The system learns from accumulated interaction data to adjust what constitutes a meaningful discomfort event versus normal variation, reducing false alerts while maintaining reliable maintenance timing.
Data Source
AI summary
A controller for an HVAC system comprises an interface, a memory, and a processor. The interface receives data related to user interactions with the system, and receives a temperature of the enclosed space. The memory stores an accumulated user discomfort value and discomfort score values, associated with user interactions. The processor determines a difference between the enclosed space temperature and a setpoint temperature. The processor determines whether the temperature difference exceeds a first threshold, and in response to determining that, determine whether a user has interacted with the system. In response to determining that the user has interacted with the system, the processor determines a current user discomfort value based on the discomfort score values, updates the accumulated user discomfort value, and determines whether the accumulated user discomfort value exceeds a second threshold. In response to determining that, the processor generates an alert indicating that a user discomfort event has occurred.