Building Control System Break Even Temperature Uncertainty
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Solution Overview
Problem
Existing methods for determining the uncertainty in building energy use models, particularly the balance point parameters, are challenging and do not allow for accurate calculation of uncertainties in these parameters, limiting the precision of energy efficiency analysis and optimization.
Innovation Solution
A method that involves receiving an energy use model, calculating the gradient of its output with respect to model parameters, determining a covariance matrix, and using this matrix to identify uncertainties in the model parameters, including balance point parameters, by incorporating regression coefficients and time duration variables, and updating the model with these uncertainties for improved analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If previous modeling techniques are used to estimate parameters in building energy use models, then some parameters can be estimated with their uncertainties determined, but the uncertainty in balance point parameter estimates cannot be calculated
Solution Approach 1:
The patent combines balance point parameters with regression model coefficients into a unified parameter estimation framework. By treating balance point parameters and regression coefficients together in a single covariance matrix calculation, the system enables uncertainty determination for all parameters simultaneously, resolving the limitation of previous techniques that could only handle regression coefficients.
2Adaptability or versatility
If balance point parameters are included in the energy use model, then the model can define temperature ranges for heating and cooling requirements, but the uncertainty associated with these parameters remains difficult to determine
Solution Approach 1:
The patent introduces a covariance matrix as an intermediary mathematical structure that facilitates uncertainty determination for balance point parameters. The covariance matrix serves as a mediator that translates model outputs and predictor variables into quantifiable uncertainty measures, making it possible to assess parameter reliability without complicating the underlying energy use model functionality.
3Measurement precision
If gradient calculation and covariance matrix determination are performed, then uncertainty in model parameters can be identified, but the computational complexity increases
Solution Approach 1:
The patent performs gradient calculations and covariance matrix determinations as preliminary steps in the modeling process, before final parameter estimation and uncertainty analysis. By preparing these computational components in advance, the system streamlines the overall workflow and reduces the computational burden during the actual parameter fitting and uncertainty quantification phases.
Data Source
AI summary
A building control system determines the uncertainty in a break even temperature parameter of an energy use model. The energy use model is used to predict energy consumption of a building site as a function of the break even temperature parameter and one or more predictor variables. The uncertainty in the break even temperature parameter is used to analyze energy performance of the building site.