Method of auto association of HVAC energy with control signal for self diagnostics of the HVAC system
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Solution Overview
Problem
Current HVAC systems lack the ability to effectively combine energy usage monitoring with control data to detect and predict potential failures, relying on isolated measurements that fail to characterize load groups or identify equipment issues proactively.
Innovation Solution
A method and system that correlate HVAC control modes with energy usage patterns using a microcontroller and energy usage collection interface, allowing for the identification of deviations from normal patterns and enabling proactive maintenance alerts by integrating energy usage data with control signals and other measured variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If energy monitoring devices are used to measure energy at different load points, then energy measurement capability is improved, but the ability to group or characterize loads and detect equipment issues remains limited
Solution Approach 1:
The patent combines energy usage data with HVAC control data into a unified system that correlates energy patterns with control modes. This merging allows the system to group loads by their control associations and characterize them based on combined operational patterns, transforming isolated energy measurements into meaningful load groupings that enable equipment issue detection.
Solution Approach 2:
The system performs multiple functions using the same data collection infrastructure: it measures energy usage, groups loads by control association, characterizes load patterns, and detects equipment issues. This multi-functionality transforms a simple energy measurement device into a comprehensive diagnostic system that addresses both measurement precision and information completeness.
2Measurement precision
If isolated energy measurements are taken at load points, then individual load measurement is achieved, but the ability to identify equipment issues proactively is lost
Solution Approach 1:
The system establishes feedback loops by continuously monitoring both energy usage and HVAC control modes, then comparing actual patterns against expected patterns. When deviations are detected—such as energy consumption that doesn't match the associated control mode—the system generates alerts for potential equipment issues. This feedback mechanism transforms static measurements into dynamic diagnostic information.
Solution Approach 2:
The system performs preliminary pattern matching and baseline establishment during normal operation, creating expected energy patterns for different HVAC control modes. This preliminary action enables the system to proactively detect issues before they manifest as failures, as it can compare real-time measurements against pre-established norms and alert operators to anomalies before they become critical problems.
3Ease of operation
If energy usage data is collected without correlating to control signals, then data collection simplicity is maintained, but diagnostic capability is reduced
Solution Approach 1:
The patent uses HVAC control signals as an intermediary that links energy usage data to specific equipment and operational modes. By introducing this intermediary layer, the system maintains the simplicity of separate data collection while enabling sophisticated correlation analysis. The control signals act as a bridge that connects energy measurements to their contextual meaning, allowing automatic association without complex reconfiguration of the data collection infrastructure.
Data Source
AI summary
A system and method are described for self-diagnosing heating, ventilation and air conditioning (HVAC) systems. The system and method correlate an HVAC control mode of a particular HVAC unit with an energy usage for the particular HVAC unit in order to match normal patterns in energy usage with the HVAC control mode. This correlation allows the system and method to identify potential problems with the particular HVAC unit based on deviations from normal patterns.