HVAC Predictive Model Calibration for Accurate Load Prediction
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Solution Overview
Problem
Existing HVAC systems face challenges in accurately predicting and controlling heating or cooling loads due to the complexity and non-linearity of building thermal dynamics, leading to inefficiencies and increased operational costs.
Innovation Solution
A predictive model calibration process using a controller with a processor and non-transitory computer-readable media to adjust model-predicted heating or cooling loads based on actual loads, employing equation-based or model-based calibration techniques to generate a calibrated predictive model for precise control of HVAC equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a predictive model is used to calculate heating or cooling load, then the control of HVAC equipment can be optimized, but the accuracy of load prediction is insufficient due to the complexity and non-linearity of building thermal dynamics
Solution Approach 1:
The patent applies parameter changes by introducing calibration parameters (scaling factors and offset values) that modify the predictive model's output parameters. The calibration model transforms the original model-predicted load values into calibrated load values through parameter adjustment, thereby improving prediction accuracy without changing the fundamental model structure or increasing complexity.
Solution Approach 2:
The patent implements feedback by using actual measured load values to calibrate the predictive model. The calibration process continuously compares model predictions with actual measurements and adjusts calibration parameters accordingly, creating a closed-loop system that improves accuracy over time while maintaining the original simple model structure.
2Measurement precision
If model calibration is performed using regression processes, then the accuracy of predictive values is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by implementing a simplified calibration approach that uses only the necessary subset of calibration parameters (scaling factor and offset value) rather than recalibrating the entire model. This selective calibration achieves sufficient accuracy improvement without requiring exhaustive computational processing, thereby reducing time loss.
Solution Approach 2:
The patent uses parameter changes to transform the calibration problem into a simple parameter optimization task. By changing only the calibration parameters rather than the entire model structure, the computational complexity is significantly reduced while still achieving improved prediction accuracy, thus minimizing processing time.
3Measurement precision
If calibration parameters are adjusted to improve model accuracy, then the predictive performance is enhanced, but the practical representativeness of parameters in building thermal dynamics may be compromised
Solution Approach 1:
The patent applies segmentation by separating the calibration parameters from the physical model parameters. The calibration parameters (scaling factor and offset) are distinct from the underlying building thermal dynamics parameters, allowing accuracy improvement without compromising the physical representativeness of the original model parameters. This segmentation enables independent optimization of accuracy and physical fidelity.
Solution Approach 2:
The patent introduces calibration parameters as intermediaries between the predictive model and actual measurements. These intermediary parameters adjust the model output without directly modifying the physical model structure, thereby maintaining the physical representativeness of original parameters while achieving improved accuracy through the intermediary calibration layer.
Data Source
AI summary
A controller for HVAC equipment uses a predictive model for the HVAC equipment to calculate a plurality of values of a model-predicted heating or cooling load of the HVAC equipment at a plurality of time steps within a time period, obtains a plurality of values of an actual heating or cooling load of the HVAC equipment at the plurality of time steps within the time period, generates a calibration model that relates the model-predicted heating or cooling load to the actual heating or cooling load using the plurality of values of the model-predicted heating or cooling load and the plurality of values of the actual heating or cooling load, uses the calibration model to calculate calibrated values of the model-predicted heating or cooling load of the HVAC equipment, and operates the HVAC equipment using the calibrated values of the model-predicted heating or cooling load.