公开(公告)号：US20210191343A1

公开(公告)日：2021-06-24

申请号：US16726007

申请日：2019-12-23

Applicant: Johnson Controls Technology Company

Inventor： Young M. Lee ,  Zhanhong Jiang ,  Kirk Drees ,  Michael Risbeck

IPC: G05B13/02 ,  G05B13/04 ,  G06N3/04 ,  G06N3/08 ,  F24F11/30 ,  F24F11/64

Abstract: Systems and methods for training a surrogate model for predicting system states for a building management system based on generated data from a simulation model are disclosed herein. The simulation model is calibrated for a building of interest. The building of interest includes building equipment operable to control a variable state of the building. The simulated data of system states are generated using the calibrated simulation model. A surrogate model is trained based on the simulated data of system states from the calibrated simulation model. System state predictions are generated using the surrogate model. The surrogate model is re-trained based on updated operational data. An updated series of system state predictions is generated using the re-trained surrogate model.

公开(公告)号：US20210190364A1

公开(公告)日：2021-06-24

申请号：US16725961

申请日：2019-12-23

Applicant: Johnson Controls Technology Company

Inventor： Young M. Lee ,  Zhanhong Jiang ,  Viswanath Ramamurti ,  Sugumar Murugesan ,  Kirk H. Drees ,  Michael James Risbeck

IPC: F24F11/64 ,  F24F11/47

Abstract: Systems and methods for training a reinforcement learning (RL) model for HVAC control are disclosed herein. Simulated experience data for the HVAC system is generated or received. The simulated experience data is used to initially train the RL model for HVAC control. The HVAC system operates within a building using the RL model and generates real experience data. A determination may be made to retrain the RL model. The real experience data is used to retrain the RL model. In some embodiments, both the simulated and real experience data are used to retrain the RL model. Experience data may be sampled according to various sampling functions. The RL model may be retrained multiple times over time. The RL model may be retrained less frequently over time as more real experience data is used to train the RL model.

公开(公告)号：US20210191348A1

公开(公告)日：2021-06-24

申请号：US16726038

申请日：2019-12-23

Applicant: Johnson Controls Technology Company

Inventor： Young M. Lee ,  Zhanhong Jiang ,  Kirk Drees ,  Michael Risbeck

IPC: G05B13/04 ,  F24F11/64

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.

公开(公告)号：US20210191342A1

公开(公告)日：2021-06-24

申请号：US16725999

申请日：2019-12-23

Applicant: Johnson Controls Technology Company

Inventor： Young M. Lee ,  Zhanhong Jiang ,  Viswanath Ramamurti ,  Sugumar Murugesan ,  Kirk H. Drees ,  Michael James Risbeck

IPC: G05B13/02 ,  G06N3/08 ,  G06N3/04 ,  G05B13/04 ,  F24F11/30 ,  F24F11/64

Abstract: Systems and methods for training a reinforcement learning (RL) model for HVAC control are disclosed herein. A calibrated simulation model is used to train a surrogate model of the HVAC system operating within a building. The surrogate model is used to generate simulated experience data for the HVAC system. The simulated experience data can be used to train a reinforcement learning (RL) model of the HVAC system. The RL model is used to control the HVAC system based on the current state of the system and the best predicted action to perform in the current state. The HVAC system generates real experience data based on the actual operation of the HVAC system within the building. The real experience data is used to retrain the surrogate model, and additional simulated experience data is generated using the surrogate model. The RL model can be retrained using the additional simulated experience data.