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    ADAPTIVE TRAINING AND DEPLOYMENT OF SINGLE CHILLER AND CLUSTERED CHILLER FAULT DETECTION MODELS FOR CONNECTED CHILLERS
    21.
    发明申请
    ADAPTIVE TRAINING AND DEPLOYMENT OF SINGLE CHILLER AND CLUSTERED CHILLER FAULT DETECTION MODELS FOR CONNECTED CHILLERS 审中-公开

    公开(公告)号：US20190385070A1

    公开(公告)日：2019-12-19

    申请号：US16198456

    申请日：2018-11-21

    Applicant: Johnson Controls Technology Company

    Inventor： Young M. Lee ,  Sugumar Murugesan ,  ZhongYi Jin ,  Jaume Amores ,  Kelsey Carle Schuster ,  Steven R. Vitullo ,  Henan Wang

    IPC: G06N5/04 ,  G06N20/00 ,  G05B13/04 ,  G05B13/02 ,  F24F11/38 ,  F24F11/63

    Abstract: A chiller fault prediction 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 receive chiller data for a plurality of chillers, the chiller data indicating performance of the plurality of chillers. The one or more processors are configured to generate, based on the received chiller data, a plurality of single chiller prediction models and a plurality of cluster chiller prediction models, the plurality of single chiller prediction models generated for each the plurality of chillers and the plurality of cluster chiller prediction models generated for chiller clusters of the plurality of chillers. The one or more processors are configured to label each of the plurality of single chiller prediction models and the plurality of cluster chiller prediction models as an accurately predicting chiller model or an inaccurately predicting chiller model based on a performance of each of the plurality of single chiller prediction models and a performance of each of the plurality of cluster chiller prediction models. The one or more processors are configured to predict a chiller fault with each of the plurality of single chiller prediction models labeled as the accurately predicting chiller models. The one or more processors are configured to predict a chiller fault for each of a plurality of assigned chillers assigned to one of a plurality of clusters labeled as the accurately predicting chiller model.

    AUTOMATIC THRESHOLD SELECTION OF MACHING LEARNING/DEEP LEARNING MODEL FOR ANOMALY DETECTION OF CONNECTED CHILLERS
    22.
    发明申请
    AUTOMATIC THRESHOLD SELECTION OF MACHING LEARNING/DEEP LEARNING MODEL FOR ANOMALY DETECTION OF CONNECTED CHILLERS 审中-公开

    公开(公告)号：US20190383510A1

    公开(公告)日：2019-12-19

    申请号：US16198377

    申请日：2018-11-21

    Applicant: Johnson Controls Technology Company

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

    IPC: F24F11/38 ,  G05B13/04 ,  G05B13/02 ,  F24F11/64

    Abstract: A chiller threshold 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 prediction models. The one or more processors are further configured to generate a first threshold evaluation value for each of the plurality of chiller 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 threshold evaluation value for each of the chiller 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 a first threshold for each of the plurality of chiller prediction models based on the first threshold evaluation values in response to the determination that chiller fault data exists in the chiller data, and select a second threshold for each of the plurality of chiller prediction models based on the second threshold evaluation values in response to the determination that chiller fault data does not exist in the chiller data.

    BUILDING MANAGEMENT AUTONOMOUS HVAC CONTROL USING REINFORCEMENT LEARNING WITH OCCUPANT FEEDBACK
    24.
    发明申请
    BUILDING MANAGEMENT AUTONOMOUS HVAC CONTROL USING REINFORCEMENT LEARNING WITH OCCUPANT FEEDBACK 审中-公开

    公开(公告)号：US20190353378A1

    公开(公告)日：2019-11-21

    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 ,  G05B13/02 ,  F24F11/65

    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.

    BUILDING SYSTEM WITH A RECOMMENDATION ENGINE THAT OPERATES WITHOUT STARTING DATA
    25.
    发明申请
    BUILDING SYSTEM WITH A RECOMMENDATION ENGINE THAT OPERATES WITHOUT STARTING DATA 有权

    公开(公告)号：US20210383276A1

    公开(公告)日：2021-12-09

    申请号：US17339322

    申请日：2021-06-04

    Applicant: Johnson Controls Technology Company

    Inventor： Vish Ramamurti ,  Wenwen Zhao ,  Chenlu Zhang ,  Kelsey Carle Schuster ,  Young M. Lee

    IPC: G06N20/00 ,  G06N5/04 ,  G05B13/02

    Abstract: A building system including one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to generate building recommendations based on recommendation requests with a model of a first model type when less than a predefined amount of model training data is available and receive feedback data on the recommendation requests generated by the model of the first model type. The instructions cause the one or more processors to transition from generating the building recommendations by the model of the first model type to a second model of a second model type by comparing performance of the first model type to the second model type based on the feedback data.

    COST SAVINGS FROM FAULT PREDICTION AND DIAGNOSIS
    26.
    发明申请
    COST SAVINGS FROM FAULT PREDICTION AND DIAGNOSIS 有权

    公开(公告)号：US20210262689A1

    公开(公告)日：2021-08-26

    申请号：US17318877

    申请日：2021-05-12

    Applicant: Johnson Controls Technology Company

    Inventor： Priti SHINDE ,  Kathiresan RAJAGOPAL ,  Abu Bakr KHAN ,  Young M. Lee

    IPC: F24F11/63 ,  G06N20/10 ,  G06Q50/06 ,  G05B19/042

    Abstract: A heating, ventilation, and air conditioning (HVAC) fault prediction system for a building including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive HVAC data relating to a plurality of HVAC components, the HVAC data indicating performance of the plurality of HVAC components, generate, based on the received HVAC data, a univariate prediction model and a multivariate prediction model, generate, using the received HVAC data, one or more predicted operational parameters for the plurality of HVAC components corresponding to a future time period, and execute at least one of the univariate prediction model or the multivariate prediction model on the one or more predicted operational parameters to predict a HVAC fault associated with at least one of the plurality of HVAC components to occur during the future time period.

    BUILDING SYSTEM WITH ADAPTIVE FAULT DETECTION
    27.
    发明申请
    BUILDING SYSTEM WITH ADAPTIVE FAULT DETECTION 有权

    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.

    ADAPTIVELY LEARNING SURROGATE MODEL FOR PREDICTING BUILDING SYSTEM DYNAMICS FROM SYSTEM IDENTIFICATION MODEL
    29.
    发明申请
    ADAPTIVELY LEARNING SURROGATE MODEL FOR PREDICTING BUILDING SYSTEM DYNAMICS FROM SYSTEM IDENTIFICATION MODEL 有权

    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.

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