Probabilistic Meta-Learning for Digital Twin Calibration
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Solution Overview
Problem
Current calibration methodologies for industrial systems, such as HVAC systems, often ignore multi-source datasets and perform calibration 'from scratch' for each new task, leading to suboptimal energy consumption and inefficient operation.
Innovation Solution
A calibration system and method that utilizes meta-learning techniques to leverage multi-source data, incorporating metadata to optimize parameter selection for industrial systems, enabling efficient calibration and control optimization by training on probabilistic distributions of performance functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-source datasets are utilized with meta-learning techniques, then calibration efficiency and convergence are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary meta-learning training on multi-source datasets from multiple buildings before actual calibration tasks. This pre-training phase extracts transferable knowledge and patterns that accelerate subsequent calibration processes, allowing the system to leverage archived data from other buildings to improve convergence speed for new calibration tasks.
Solution Approach 2:
The patent introduces probabilistic meta-learning as an intermediary layer between multi-source datasets and target building calibration. This intermediary processes and synthesizes information from multiple sources, managing the complexity of integrating diverse datasets while enabling efficient knowledge transfer to the target system.
2Measurement precision
If calibration is performed from scratch for each new task, then data accuracy for the specific task is maintained, but time consumption and computational resources increase
Solution Approach 1:
The system performs preliminary meta-learning training on multi-source datasets from multiple buildings before actual calibration tasks. This pre-training phase extracts transferable knowledge and patterns that accelerate subsequent calibration processes, allowing the system to leverage archived data from other buildings to improve convergence speed for new calibration tasks.
Solution Approach 2:
The patent modifies the calibration approach by changing from direct parameter optimization to probabilistic parameter distribution learning. Instead of finding single optimal parameters, the system learns probability distributions over parameters, enabling faster convergence while maintaining accuracy through uncertainty quantification and adaptive sampling.
3Ease of manufacture
If archived multi-source datasets are ignored, then calibration process remains simple, but energy optimization potential is lost
Solution Approach 1:
The system implements feedback loops where calibration results and performance data from target buildings are fed back into the multi-source dataset repository. This continuous feedback mechanism allows the archived data to improve over time, enabling the system to capture energy optimization opportunities while managing complexity through iterative learning.
Solution Approach 2:
The patent enables the calibration system to self-improve by automatically incorporating new calibration results and performance data into the archived multi-source datasets. This self-service mechanism allows the system to autonomously capture energy optimization patterns from each new building, reducing manual intervention while improving future calibration efficiency.
Data Source
AI summary
A controller and a method for optimizing a controlled operation of a system performing a task is provided. The method for optimizing the controlled operation of the system comprises accessing a probabilistic distribution of a performance function trained to provide a relationship between different combinations of control parameters for controlling the system and their corresponding costs of operation, selecting a combination of control parameters from the different combinations of control parameters, such that the selected combination of control parameters is having the largest likelihood of being optimal at the probabilistic distribution of the performance function. The method further comprises controlling the system using the selected combination of the control parameters and modifying the probabilistic distribution of the performance function conditioned on the selected combination of the control parameters and the corresponding cost of operation.