HVAC Return Time Estimation Using Controller Output Signal Filtering
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Solution Overview
Problem
Existing HVAC control systems face challenges in accurately estimating the time required to cool down or warm up a building zone from an unoccupied setback condition, often relying on outside air temperature measurements and being computationally expensive or requiring fixed parameter values that reduce adaptability.
Innovation Solution
A method and system that estimate the return time by determining cooling or heating demands using a controller's output signal, filtered through signal filters, and an empirical model with learned parameters, allowing for adaptive adjustments without additional sensors or complex computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If outside air temperature measurements are used to estimate return time, then the estimation can be performed, but the system complexity increases and adaptability is reduced due to fixed parameter requirements
Solution Approach 1:
The patent extracts the return time estimation function from complex external sensor systems and implements it using only existing HVAC controller data. By taking out the dependency on outside air temperature measurements and replacing it with internal controller output signal analysis, the system achieves the same estimation function with reduced complexity and no additional sensors.
Solution Approach 2:
The HVAC controller serves itself by using its own existing output signals and internal processing capabilities to estimate return time. The controller analyzes its own controller output signal during unoccupied periods and applies filtering and empirical modeling internally, eliminating the need for external measurement systems and reducing overall system complexity.
2Measurement precision
If complex computations are used to estimate return time, then accuracy may improve, but energy consumption and computational cost increase
Solution Approach 1:
The patent applies partial action by using only the necessary portion of computational processing - specifically, simple filtering operations and basic empirical model calculations on existing controller signals. This avoids excessive computational action that would consume more energy, while still achieving sufficient estimation accuracy for HVAC control purposes.
Solution Approach 2:
The patent changes the parameters being analyzed from external temperature measurements requiring complex environmental modeling to internal controller output signals that can be processed with simple filtering and basic empirical relationships. This parameter transformation reduces computational complexity and energy consumption while maintaining estimation accuracy.
3Ease of manufacture
If fixed parameter values are used in the estimation model, then the model is simpler to implement, but adaptability to different building zones and conditions is reduced
Solution Approach 1:
The patent implements dynamics by making the estimation model adaptive to different building zones through learning algorithms that adjust parameters based on actual zone behavior. The controller learns zone-specific characteristics over time, allowing the same basic model structure to adapt to different zones, buildings, and environmental conditions without requiring complex reconfiguration.
Solution Approach 2:
The patent creates a universal estimation model that can be applied across different building zones and conditions. By using a standardized filtering and empirical modeling approach that learns zone-specific parameters, the same model structure serves multiple zones and building types, achieving both implementation simplicity and broad adaptability.
4Measurement precision
If additional sensors are installed to improve return time estimation, then measurement accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent extracts the measurement function from external sensors and relocates it to the existing HVAC controller by analyzing the controller's own output signals. This eliminates the need for additional temperature sensors or other measurement devices, maintaining estimation accuracy while reducing device complexity and cost.
Solution Approach 2:
The HVAC controller performs the measurement function for itself by monitoring and analyzing its own controller output signal during unoccupied periods. This self-service approach eliminates dependency on external sensors, reducing system complexity while providing the necessary measurement data for return time estimation.
Data Source
AI summary
Systems and methods for estimating a time to cool down or warm up a building zone from a temperature setback condition are provided. A described method includes determining, by a controller for the building zone, at least one of a cooling demand for the building zone and a heating demand for the building zone for a time period corresponding to the temperature setback condition. The method further includes estimating a return time using at least one of the cooling demand and the heating demand. The return time is the time to cool down or warm up the building zone from the temperature setback condition.