Building Management Data Tagging for Faster Commissioning
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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 generating digital representations of physical spaces, configuring devices, and manually transferring data.
Innovation Solution
A building management platform that automatically tags data points using context data, executes machine learning models to generate suggested tags, and updates models based on user feedback, dynamically controlling environmental variables to validate tags and identify anomalies, thereby reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual commissioning and configuration methods are used for building management systems, then data accuracy can be ensured through human review, but the process becomes time-intensive and costly
Solution Approach 1:
The system enables automatic self-configuration and commissioning through machine learning models that autonomously tag data points, generate digital representations, and validate configurations without requiring manual human intervention for each task
Solution Approach 2:
Manual commissioning processes are replaced with automated machine learning-based systems that use algorithms to tag data points, create digital representations, and validate configurations, substituting human mechanical work with computational processes
2Measurement precision
If complete manual review of all data points is performed, then tagging accuracy is maximized, but the commissioning process becomes excessively time-consuming
Solution Approach 1:
The system performs automatic tagging for all data points using machine learning models, then applies selective manual review only to specific cases based on confidence thresholds, rather than requiring complete manual review of every data point
Solution Approach 2:
The system implements feedback loops where manual review results are used to retrain and improve the machine learning models, allowing the system to learn from corrections and improve accuracy over time while maintaining high-speed automated processing
3Productivity
If machine learning models are used to automatically tag data points, then commissioning speed increases, but the initial setup and model training require significant time and resources
Solution Approach 1:
The system performs preliminary actions by pre-tagging data points using machine learning models before manual review, and by pre-generating digital representations and configurations, so that the bulk of the commissioning work is already completed when human reviewers examine the results
Solution Approach 2:
The system creates digital representations (digital twins) that are copies of the physical building spaces and equipment, allowing the system to work with these digital copies during commissioning rather than directly manipulating physical systems, thereby accelerating the process
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.