Adaptively learning surrogate model for predicting building system dynamics from system identification model
US11409250B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 23, 2019 |
| Grant date | Aug 9, 2022 |
| Priority date | — |
| Expiry date | Mar 7, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/25011
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
Systems and methods for training a surrogate model for predicting system states for a building management system based on generated data from a system identification model are disclosed herein. The system identification model is used to generate predicted system parameters of a zone of the building based on historic data from operation of the building equipment. The surrogate model is trained based on the predicted system parameters from the system identification model. Predicted future parameters of the variable state of the building are generated using the surrogate model. The surrogate model is re-trained based on new operational data from the building equipment. An updated series of predicted future parameters is generated using the re-trained surrogate model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.