Building Thermal Resistance Estimation Using Data Assimilation
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Solution Overview
Problem
Current methods for estimating the thermal resistance of buildings are limited by their inability to account for thermal inertia, representativeness biases, and the influence of multiple heating sources, leading to inaccurate temperature regulation and energy management.
Innovation Solution
A method that measures interior and exterior temperatures, and heating power over a specified time interval, using data assimilation techniques to minimize a functional under constraints, incorporating a filter to account for thermal inertia and allowing for multiple heating sources, thereby improving the estimation of thermal resistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a simple least-squares method is used to estimate thermal resistance from temperature and heating power measurements, then the implementation is simple, but the estimation accuracy is reduced due to inability to account for thermal inertia and representativeness biases
Solution Approach 1:
The patent transforms the estimation problem from a simple linear regression to an optimization problem where parameters including thermal resistance, thermal inertia coefficients, and representativeness bias factors are simultaneously adjusted to minimize the difference between measured and modeled temperatures. This allows the model to account for thermal inertia and measurement biases while maintaining a computationally tractable approach.
Solution Approach 2:
The patent introduces an intermediary mathematical model that includes thermal inertia terms and bias correction factors between the raw measurements and the thermal resistance estimation. This intermediary model acts as a bridge that reconciles the simplicity of direct measurement with the complexity of thermal dynamics, allowing accurate estimation without requiring complex instrumentation.
2Measurement precision
If thermal inertia of the building structure is accounted for using complex measurement instrumentation and minimization algorithms, then the estimation accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent employs standard temperature and power sensors already present in typical building management systems to collect data. The complexity of accounting for thermal inertia is handled through software-based optimization algorithms that process the measurements, rather than through complex hardware instrumentation. The existing measurement infrastructure serves the enhanced estimation function without requiring additional specialized equipment.
Solution Approach 2:
The patent replaces the need for complex physical measurement systems with a computational approach. Instead of using sophisticated instrumentation to directly measure thermal properties, the system uses standard sensors combined with mathematical modeling and optimization to infer thermal resistance, effectively substituting computational complexity for hardware complexity.
3Ease of operation
If local inside temperature measurements are used without correction, then the measurement is straightforward, but representativeness biases occur that reduce estimation accuracy
Solution Approach 1:
The patent applies a representativeness bias correction factor to the local temperature measurements during the optimization process. This preliminary correction adjusts the measured temperatures to better represent the average building temperature before using them in the thermal resistance estimation, accounting for spatial variations without requiring extensive sensor networks.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a more precise and simplified estimation of thermal resistance, accounting for thermal inertia and multiple heating sources, enhancing the accuracy of temperature regulation and energy management in buildings.
Implementation Method 1
a first filter, with a time constant on the order of a few hours classically, to represent the inertia of the building structure
Implementation Method 2
The thermal resistance GV actually corresponds to the additional heating power required to raise the air temperature in the building by 1°C
Data Source
Figure 1
AI summary
The invention relates to a method for determining the thermal resistance (GV) of a building comprising the steps of: a) measuring an internal temperature Tintmes of the building, b) measuring an external temperature (Text) of the building, c) measuring a heating power (Pmes) of the building, d) repeating steps a), b) and c) at a time step (P) over a total time interval (T), e) synchronizing the measurements of steps a), b) and c) at the same time step so as to obtain a set Tintmesj,Textj,Pmesjj=0…N of N triplets (N being equal to T divided by P) of values of internal temperature of the building, external temperature of the building and heating power of the building at all times (j) of measurement of steps a), b) and c), f) estimating the thermal resistance (GV) of the building, step f) comprising a step of minimizing a functional (J) subject to the constraint of the physical relationship between the heating power,the outside temperature and the inside temperature of the building, relative to the thermal resistance (GV) of the building, characterized in that the minimization of the functional (J) is also done relative to a Tintest estimate of the inside temperature of the building and to X scalars (ai)i=1...X constituting the parameters of an X-order filter with unity gain of the outside temperature (Text) of the building, and in that it further comprises a step g) of providing data relating to the value of the thermal resistance (GV) estimated during step f).,