Building Equipment Control Using Synthetic Data Compliance Screening
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Solution Overview
Problem
Existing control systems for building equipment lack the ability to predictively model and ensure compliance with regulations and certification standards, leading to potential operational inefficiencies and non-compliance.
Innovation Solution
A method and system that utilize a generative artificial intelligence model to generate building data, determine compliance with regulations and certification standards, and adjust the operation of building equipment accordingly, while also filtering out non-compliant data and fine-tuning the AI model for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If building equipment is operated without compliance verification, then operational simplicity is maintained, but regulatory compliance cannot be ensured
Solution Approach 1:
The system performs compliance verification in advance by generating synthetic building data, evaluating it against regulations and standards, and determining compliance status before actual equipment operation. This preliminary action ensures compliance is established beforehand, avoiding the need for complex real-time monitoring and adjustment mechanisms during operation.
Solution Approach 2:
The system creates synthetic copies of building data that represent various operational scenarios and uses these copies for compliance evaluation. By working with synthetic data copies rather than actual operational data, the system can verify compliance without interfering with real equipment operation, thus maintaining simplicity while ensuring reliability.
2Reliability
If all generated building data is used for equipment operation, then data utilization is maximized, but non-compliant data may cause regulatory violations
Solution Approach 1:
The system extracts and identifies non-compliant portions of generated building data through evaluation against regulations and standards. By separating compliant data from non-compliant data, the system ensures only compliant portions are used for equipment operation, preventing regulatory violations while maintaining efficient use of valid data.
Solution Approach 2:
The system implements a feedback mechanism where generated building data is evaluated for compliance, and the results feed back into the data generation and selection process. This feedback loop ensures that non-compliant data is identified and excluded, while compliant data is utilized, maintaining both compliance adherence and data utilization efficiency.
3Measurement precision
If the generative AI model is not fine-tuned, then system simplicity is maintained, but data accuracy and compliance quality deteriorate
Solution Approach 1:
The system performs fine-tuning of the generative AI model in advance using compliant building data as training input. By pre-fine-tuning the model with high-quality compliant data, the system improves the accuracy and compliance quality of generated data without requiring complex real-time adjustments. The fine-tuning is performed beforehand, reducing the need for ongoing complex model maintenance.
Data Source
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AI summary
A method for controlling building equipment includes generating building data relating to conditions in a building space using a generative artificial intelligence model. The method also includes determining whether the building data correspond to conditions in the building space that comply with one or more regulations or certification standards. The method includes, in response to determining that the building data correspond to conditions in the building space that comply with the one or more regulations or certification standards, using the building data to operate building equipment that serve the building space.