公开(公告)号：US20210191379A1

公开(公告)日：2021-06-24

申请号：US16725940

申请日：2019-12-23

Applicant: Johnson Controls Technology Company

Inventor： Jaume Amores Llopis ,  Young M. Lee ,  Sugumar Murugesan ,  Steven R. Vitullo

IPC: G05B23/02

Abstract: A building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a plurality of times within a first time period; determine a first time at which a first cumulative sum value is at a first maximum; identify a second cumulative sum value at a second maximum at a second time occurring after the first time; compare the identified second cumulative sum value to a threshold; and based on determining that the identified second cumulative sum value does not exceed the threshold, determine that a first fault ended at the first time.

公开(公告)号：US20210191378A1

公开(公告)日：2021-06-24

申请号：US16725644

申请日：2019-12-23

Applicant: Johnson Controls Technology Company

Inventor： Jaume Amores ,  Young M. Lee ,  Sugumar Murugesan ,  Steven R. Vitullo

IPC: G05B23/02 ,  G05B15/02 ,  G06F11/07 ,  G06Q10/06

Abstract: A method of generating a fault determination in a building management system (BMS), the method including receiving signal data, generating, using a number of fault detection models, a number of fault indications based on the signal data, generating, using a weighting function, based on the number of fault indications, a fault score, comparing the fault score to a fault value, and determining, based on the comparison, an existence of a fault.

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.

公开(公告)号：US20210116874A1

公开(公告)日：2021-04-22

申请号：US16657398

申请日：2019-10-18

Applicant: Johnson Controls Technology Company

Inventor： Sugumar Murugesan ,  Young M. Lee ,  Jaume Amores Llopis

IPC: G05B13/04 ,  G06Q50/06 ,  G01D4/00

Abstract: A building management system including building equipment operable to affect a variable state or condition of a building. The building management system includes a controller including a processing circuit. The processing circuit is configured to obtain an energy prediction model (EPM) for predicting energy requirements over time. The processing circuit is configured to monitor one or more triggering events to determine if the EPM should be retrained. The processing circuit is configured to, in response to detecting that a triggering event has occurred, identify updated values of one or more hyper-parameters of the EPM. The processing circuit is configured to operate the building equipment based on the EPM.

公开(公告)号：US10852023B2

公开(公告)日：2020-12-01

申请号：US15980547

申请日：2018-05-15

Applicant: Johnson Controls Technology Company

Inventor： Viswanath Ramamurti ,  Young M. Lee ,  Youngchoon Park ,  Sugumar Murugesan

IPC: F24F11/50 ,  F24F11/65 ,  G05B13/02 ,  F24F120/20 ,  F24F120/12

Abstract: A building management system includes one or more processors, and one or more computer-readable storage media communicably coupled to the one or more processors and having instructions stored thereon that cause the one or more processors to: define a state of a zone or space within a building; control an HVAC system to adjust a temperature of the zone or space corresponding to a first action; receive utterance data from a voice assist device located in the zone or space; analyze the utterance data to identify a sentiment relating to the temperature of the zone or space; calculate a reward based on the state, the first action, and the sentiment; determine a second action to adjust the temperature of the zone or space based on the reward; and control the HVAC system to adjust the temperature of the zone or space corresponding to the second action.

公开(公告)号：US20190384239A1

公开(公告)日：2019-12-19

申请号：US16198416

申请日：2018-11-21

Applicant: Johnson Controls Technology Company

Inventor： Sugumar Murugesan ,  Young M. Lee ,  ZhongYi Jin ,  Jaume Amores

IPC: G05B13/04 ,  G05B13/02 ,  F24F11/38

Abstract: A model management system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to determine whether chiller fault data exists in chiller data used to generate a plurality of chiller shutdown prediction models. The one or more processors are further configured to generate a first performance evaluation value for each of the plurality of chiller shutdown prediction models using a first evaluation technique in response to a determination that chiller fault data exists in the chiller data, and generate a second performance evaluation value for each of the plurality of chiller shutdown prediction models using a second evaluation technique in response to a determination that chiller fault data does not exist in the chiller data. The one or more processors are configured to select one of the plurality of chiller shutdown prediction models based on the first performance evaluation in response to the determination that chiller fault data exists in the chiller data, and select one of the plurality of chiller shutdown prediction models based on the second performance evaluation in response to the determination that chiller fault data does not exist in the chiller data.