Building system with generative artificial intelligence point naming, classification, and mapping
A method for a building automation system includes performing data augmentation of a point, equipment, and subtype (PES) dataset using generative artificial intelligence to generate an augmented PES dataset. The method includes fine-tuning at least one large language model (LLM) using the augmented PES dataset, discovering points on a building network for the building automation system, performing PES classification of the points using the at least one fine-tuned LLM, and operating equipment of the building automation system using the PES classification to affect a physical condition of a building.
1. A method for a building automation system, comprising:
performing data augmentation of a point, equipment, and subtype (PES) dataset using generative artificial intelligence to generate an augmented PES dataset;
fine-tuning at least one large language model (LLM) using the augmented PES dataset;
discovering points on a building network for the building automation system;
performing PES classification of the points using the at least one fine-tuned LLM; and
operating equipment of the building automation system using the PES classification to affect a physical condition of a building.
2. The method of claim 1 , wherein performing the data augmentation of the PES dataset comprises adding synthetic data to the PES dataset, the synthetic data representing behaviors of points in the PES dataset under a plurality of different conditions or scenarios.
3. The method of claim 1 , wherein performing the data augmentation of the PES dataset comprises adding synthetic points to the PES dataset, the synthetic points associated with a plurality of equipment and subtypes that are not represented in the PES dataset.
4. The method of claim 1 , wherein performing the PES classification of the points using the at least one fine-tuned LLM comprises applying the at least one fine-tuned LLM in a chain-of-thoughts technique.
5. The method of claim 1 , wherein performing the PES classification of the points using the at least one fine-tuned LLM comprises:
generating a description of behaviors or features of the points by executing a first thought of a chain-of-thoughts using the at least one fine-tuned LLM; and
classifying equipment and subtypes for the points based on the description of the behaviors or features of the points by executing a second thought of the chain-of-thoughts using the at least one fine-tuned LLM.
6. The method of claim 1 , comprising deploying the at least one fine-tuned LLM to an edge device installed locally at the building;
wherein performing the PES classification of the points using the at least one fine-tuned LLM comprises executing the at least one fine-tuned LLM on the edge device.
7. The method of claim 1 , wherein performing the PES classification of the points using the at least one fine-tuned LLM comprises augmenting the points with point names, equipment associated with the points, and subtypes of the equipment associated with the points.
8. The method of claim 1 , comprising:
determining that the at least one fine-tuned LLM is unable to classify a subset of the points;
providing the subset of the points to a cloud computing system comprising one or more artificial intelligence models; and
classifying the subset of the points by executing the one or more artificial intelligence models at the cloud computing system.
9. The method of claim 1 , comprising:
determining that the at least one fine-tuned LLM is unable to classify a subset of the points;
providing the subset of the points to user device configured to receive human feedback; and
classifying the subset of the points based on the human feedback received via the user device.
10. The method of claim 1 , wherein operating the equipment of the building automation system using the PES classification comprises:
mapping the points to one or more setpoints or measured points in a control process based on the PES classification; and
executing the control process to affect the physical condition of the building.
11. A building system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
performing data augmentation of a point, equipment, and subtype (PES) dataset using generative artificial intelligence to generate an augmented PES dataset;
fine-tuning at least one large language model (LLM) using the augmented PES dataset;
discovering points on a building network for the building system;
performing PES classification of the points using the at least one fine-tuned LLM; and
operating equipment of the building system using the PES classification to affect a physical condition of a building.
12. The building system of claim 11 , wherein performing the data augmentation of the PES dataset comprises adding synthetic data to the PES dataset, the synthetic data representing behaviors of points in the PES dataset under a plurality of different conditions or scenarios.
13. The building system of claim 11 , wherein performing the data augmentation of the PES dataset comprises adding synthetic points to the PES dataset, the synthetic points associated with a plurality of equipment and subtypes that are not represented in the PES dataset.
14. The building system of claim 11 , wherein performing the PES classification of the points using the at least one fine-tuned LLM comprises applying the at least one fine-tuned LLM in a chain-of-thoughts technique.
15. The building system of claim 11 , wherein performing the PES classification of the points using the at least one fine-tuned LLM comprises:
generating a description of behaviors or features of the points by executing a first thought of a chain-of-thoughts using the at least one fine-tuned LLM; and
classifying equipment and subtypes for the points based on the description of the behaviors or features of the points by executing a second thought of the chain-of-thoughts using the at least one fine-tuned LLM.
16. The building system of claim 11 , the operations comprising deploying the at least one fine-tuned LLM to an edge device installed locally at the building;
wherein performing the PES classification of the points using the at least one fine-tuned LLM comprises executing the at least one fine-tuned LLM on the edge device.
17. The building system of claim 11 , wherein performing the PES classification of the points using the at least one fine-tuned LLM comprises augmenting the points with point names, equipment associated with the points, and subtypes of the equipment associated with the points.
18. The building system of claim 11 , the operations comprising:
determining that the at least one fine-tuned LLM is unable to classify a subset of the points;
providing the subset of the points to a cloud computing system comprising one or more artificial intelligence models; and
classifying the subset of the points by executing the one or more artificial intelligence models at the cloud computing system.
19. The building system of claim 11 , the operations comprising:
determining that the at least one fine-tuned LLM is unable to classify a subset of the points;
providing the subset of the points to user device configured to receive human feedback; and
classifying the subset of the points based on the human feedback received via the user device.
20. The building system of claim 11 , wherein operating the equipment of the building system using the PES classification comprises:
mapping the points to one or more setpoints or measured points in a control process based on the PES classification; and
executing the control process to affect the physical condition of the building.