Building Management Model Commissioning With Automated Data Tagging
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Solution Overview
Problem
The initial commissioning of new buildings and spaces within building management systems is time-intensive and costly due to the difficulty in manually identifying and configuring assets, generating digital representations, and integrating devices with the system.
Innovation Solution
A building management platform that automatically tags data points using context data, executes machine learning models for tag validation, and incorporates user feedback to enhance tag accuracy, while dynamically controlling environmental variables and monitoring sensor measurements to validate and update digital representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and configuration of assets is performed, then data accuracy can be ensured through human review, but the commissioning process becomes time-intensive and costly
Solution Approach 1:
The system performs self-service by automatically tagging data points using machine learning models that analyze context data from building equipment, sensors, and operational data. The system autonomously generates digital representations and configures assets without requiring manual human intervention for each data point, thereby reducing commissioning time while maintaining accuracy through automated validation mechanisms.
Solution Approach 2:
The patent replaces the mechanical manual process of identifying and configuring assets with an automated computational system. Machine learning models substitute human analysts by processing context data, generating tags, and validating data points algorithmically,ไป่ eliminating the time-consuming manual review process while preserving data accuracy through automated confidence scoring and validation protocols.
2Productivity
If automated tagging using machine learning models is implemented, then commissioning time is reduced, but initial system complexity and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models using historical building data before deployment. Context data from building information models, equipment specifications, and operational histories are processed in advance to establish baseline tagging frameworks. This preliminary preparation reduces the computational complexity during actual commissioning, as the models are already trained and ready to rapidly tag new data points without requiring complex real-time processing.
3Reliability
If comprehensive validation of data points is performed, then data reliability is improved, but the processing time and computational load increase
Solution Approach 1:
The system applies partial validation by focusing computational resources on validating only the most critical data points and those with lower confidence scores. Instead of uniformly validating every single data point, the system prioritizes validation efforts on tags that significantly impact building operations or have higher uncertainty, thereby achieving sufficient data reliability without the excessive time and computational load of comprehensive validation of all data points.
Data Source
AI summary
A system for commissioning a model, comprising one or more processing circuits configured to identify a first plurality of data points in the building, automatically tag at least a portion of the first plurality of data points with one or more first tags using context data extracted from and/or associated with the data points, the one or more entities comprising one or more of building equipment, building spaces, people, or events, identify at least one of the first plurality of data points for manual review and generate one or more suggested tags for the at least one data point, receive feedback from the manual review, and receive a second plurality of data points in the building and automatically tag at least a portion of the second plurality of data points with one or more second tags using the feedback from the manual review.