Thermostat Mis-Set Detection Using Segmented Sensor Analysis
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Solution Overview
Problem
Current methods for determining if a thermostat is correctly set are inefficient and prone to false positives, especially in environments like refrigeration units where temperature fluctuations can be detrimental.
Innovation Solution
A system and method that analyze ambient environmental data using sensors to identify aberrative behavior, remove known anomalies, and determine normal behavior patterns, allowing for the identification of mis-set thermostats by analyzing deviations from these patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional thermostat monitoring methods are used, then device complexity is low, but measurement precision and reliability are poor leading to false positives
Solution Approach 1:
The patent segments the temperature monitoring task into multiple components: scattered temperature sensors distributed throughout the environment, each independently measuring local temperature. These segmented measurements are then compiled and analyzed collectively to determine overall thermostat accuracy, improving measurement precision through distributed sensing rather than relying on a single monitoring point
Solution Approach 2:
The patent introduces an intermediary analysis system that processes temperature data from scattered sensors and compares it against thermostat setpoints. This intermediary layer includes algorithms that account for environmental factors and heat distribution patterns, serving as a mediator between raw sensor data and thermostat evaluation, thereby improving measurement precision without requiring direct complex interaction between sensors and thermostat control
2Measurement precision
If scattered sensor readings are compiled to determine thermostat accuracy, then measurement coverage is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing thermal models and heat distribution patterns for the specific environment before analysis. These pre-computed models account for expected temperature gradients, heat sources, and airflow patterns. When sensor data is collected, it is compared against these pre-established models, significantly reducing the computational complexity of detecting thermostat accuracy while maintaining high measurement precision
Data Source
AI summary
Systems and methods are provided to for identifying a mis-set, mis-calibrated, or malfunctioning thermostat. The method can include receiving ambient environmental data from at least one sensor monitoring an asset; identifying aberrative behavior in the ambient environment data; obtaining a complement of the aberrative behavior; determining a segment of normal behavior in the complement; identifying a mis-set subsequence in the segment; generating a report documenting the mis-set subsequence; and transmitting the report to a user device.