Adaptive Filter Bank for Building Heat Transfer Characterization
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Solution Overview
Problem
Existing thermal systems face challenges in accurately characterizing heat transfer without commissioning information, leading to inefficiencies and wasteful energy use due to unknown or unreliable zone geometry and thermal mass data.
Innovation Solution
An adaptive filter bank is implemented to characterize heat transfer by receiving thermal coefficient data, generating reference signal data, and modifying coefficients based on estimation errors, allowing for accurate heat transfer characterization without requiring explicit commissioning information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If thermal models use observed physical relationships and weather estimates to characterize heat transfer, then prediction accuracy of temperature and power consumption is improved, but the system requires commissioning information such as zone geometry and thermal mass data which is often unknown or unreliably reported
Solution Approach 1:
The thermal model performs self-characterization by automatically extracting heat transfer coefficients from passive observation of operational data without requiring external commissioning information. The system uses observed relationships between environmental conditions, device operations, and temperature changes to determine thermal properties autonomously, eliminating the need for manual zone geometry and thermal mass data input
Solution Approach 2:
The system continuously refines heat transfer characterization by comparing predicted temperature changes with actual observed temperature changes and adjusting thermal coefficients accordingly. This feedback mechanism allows the model to learn and adapt to the specific thermal behavior of the building over time, improving accuracy without requiring initial commissioning data
2Ease of operation
If thermal models extract information from passive observation of operational data, then commissioning information requirements are eliminated, but estimation errors occur when characterizing heat transfer
Solution Approach 1:
The thermal model transitions from static commissioning-based parameters to dynamic adaptive coefficients that continuously evolve based on observed operational data. The system dynamically adjusts thermal coefficients to match actual building behavior, allowing the model to adapt to changing conditions and improve accuracy over time without requiring initial precise measurements
Solution Approach 2:
The system changes the approach from using fixed commissioning parameters to using adaptive thermal coefficients that are continuously refined based on observed data. By modifying the thermal model parameters dynamically through passive observation and feedback, the system achieves accurate heat transfer characterization without requiring initial parameter input
Data Source
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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.