Adaptive Filter Bank for Thermal Model Self-Characterization
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Solution Overview
Problem
Existing thermal systems face challenges in accurately characterizing heat transfer without commissioning information, leading to inefficient energy use due to unknown or unreliable data about zone geometry and thermal properties.
Innovation Solution
An adaptive filter bank is implemented to characterize heat transfer by receiving thermal coefficient data, generating reference signals, and modifying coefficients based on estimation errors, allowing for passive observation of environmental conditions and power consumption without requiring specific geometry or thermal mass data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If thermal models use commissioning information to characterize heat transfer, then measurement precision improves, but device complexity increases due to requirements for geometry and thermal property data
Solution Approach 1:
The thermal model performs self-characterization by automatically extracting heat transfer parameters from operational data without requiring external commissioning information. The system uses passive observation of temperature and power consumption data to determine thermal coefficients, eliminating the need for manual geometry and thermal property input while maintaining accurate heat transfer characterization
Solution Approach 2:
The invention transitions from requiring fixed commissioning parameters (geometry, thermal mass, heat transfer coefficients) to dynamically determining these parameters from operational data. The system changes the approach from static parameter input to dynamic parameter extraction, allowing the thermal model to adapt to actual system behavior rather than relying on design specifications
2Reliability
If thermal models require commissioning information, then reliability of heat transfer characterization improves, but loss of information increases when geometry or thermal properties are unknown
Solution Approach 1:
The system uses operational data (temperature measurements and power consumption) as an intermediary to infer the thermal characteristics of the zone. Instead of directly requiring geometry and thermal property information, the model uses these observable parameters as mediators to indirectly determine the heat transfer characteristics, maintaining reliability without needing complete physical property data
3Ease of operation
If thermal systems use unknown or unreliable zone information, then ease of operation improves, but loss of energy increases due to inefficient temperature control
Solution Approach 1:
The thermal model continuously refines its heat transfer characterization by comparing predicted temperature changes with actual observations and using the estimation errors to update thermal coefficients. This feedback mechanism allows the system to achieve accurate energy-efficient control without requiring complete prior knowledge of zone properties, as the model learns and adapts from operational data over time
Data Source
AI summary
Embodiments of the disclosure implement an application of an adaptive filter bank that is used to characterize the heat transfer of a volume in a thermal system, to estimate temperature and power consumption, and to improve performance characteristics in applications including optimal temperature control and diagnostics. In some embodiments, the adaptive filter bank is an iterative solution, comprised of a collection of adaptive filters defined to consume incident signals, produce an aggregate reference signal, estimate an error relative to an observed primary signal, and modify thermal coefficients to converge on a solution. For example, the incident signals are comprised of properties related to active, passive, solar irradiance, and unobserved heat transfer. A reference signal is an estimate of a primary signal, related to the rate of heat transfer or temperature change. Thereupon, the thermal coefficients are modified in an adaptive process to include gradient descent, which minimizes estimation error.