Johnson Controls Tyco IP Holdings Llp

Johnson Controls Technology Company

Tyco Fire & Security Gmbh

Sugumar Murugesan

Young Min Lee

Jaume Amores

Viswanath Ramamurti

Steven R Vitullo

Zhongyi Jin

Kirk H Drees

Michael Risbeck

Zhanhong Jiang

Jaume Amores Llopis

Youngchoon Park

Kelsey Carle Schuster

Young M Lee

Henan Wang

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.

Robust fault detection and diagnosis by constructing an ensemble of detectors

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

Adaptive selection of machine learning/deep learning model with optimal hyper-parameters for anomaly detection of connected equipment

A method of operating a building management system is disclosed. The method includes determining, by a processing circuit, policy rankings for a plurality of control policies based on building operation data of a first previous time period, selecting, by the processing circuit, a set of control policies from among the plurality of control policies based on the policy rankings of the set of control policies satisfying a ranking threshold, generating, by the processing circuit, a plurality of prediction models for the set of control policies, selecting, by the processing circuit, a first prediction model of the plurality of prediction models based on building operation data of a second previous time period, and responsive to selecting the first prediction model, operating, by the processing circuit, the building management system using the first prediction model.

Building control system using reinforcement learning

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.

Methods and systems for training building control using simulated and real experience data

A building system for training a prediction model with augmented training data. The building system comprising 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 obtain a first training data set comprising data values associated with a data point of the building system and with a plurality of time-steps and energy values associated with consumption of the building system at each of the plurality of time-steps; generate an augmented training data set comprising a second training data set, the second training data set comprising the energy values and the data values of the first training data set but with a data value replaced with a predetermined value at a time-step of the plurality of time-steps; and generate a prediction model by training the prediction model.

Building system with model training to handle selective forecast data

A fault prediction system for building equipment includes one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to receive device data for a plurality of devices of the building equipment, the device data indicating performance of the plurality of devices; generate, based on the received device data, a plurality of prediction models comprising at least one of single device prediction models generated for each of the plurality of devices or cluster prediction models generated for device clusters of the plurality of devices; label each of the plurality of prediction models as an accurately predicting model or an inaccurately predicting model based on a performance of each of the plurality of prediction models; and predict a device fault with each of the plurality of prediction models labeled as an accurately predicting model.

Adaptive training and deployment of single device and clustered device fault detection models for connected equipment

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 the 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 first plurality of times within a first time period; analyze cumulative sum values associated with a second plurality of times occurring before the first time to identify a second time of the second plurality of times at which a second cumulative sum value is at a local minimum; and determine that a first fault began at the second time.

Building system with early fault detection

A building system for generating input forecast data. The building system comprising 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 retrieve a current prediction set of measurements comprising current values associated with a plurality of time-steps; identify time-steps of the plurality of time-steps for which historical prediction values of a historical prediction set of measurements are outside of a tolerance of corresponding historical actual values of a historical actual set of measurements; replace each current value of the current prediction set of measurements that is associated with the identified time-steps with a predetermined value to generate an updated prediction set of measurements; and provide the updated prediction set of measurements to a prediction model.

Building system with selective use of data for probabilistic forecasting

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.

Automatic threshold selection of machine learning/deep learning model for anomaly detection of connected chillers

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.

Methods and systems for training HVAC control using surrogate model

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.

Adaptive selection of machine learning/deep learning model with optimal hyper-parameters for anomaly detection of connected chillers

A building system for building data point prediction, the building system comprising one or more memory devices configured to store instructions, that, when executed by one or more processors, cause the one or more processors to receive first building data for a building data point of a building and generate training data, the training data comprising a probability distribution sequence comprising a first probability distribution for the building data point. The instructions cause the one or more processors to train a prediction model based on the training data, receive second building data for the building data point, and predict, for one or more time-steps into the future, one or more second probability distributions with the second building data based on the prediction model, each of the one or more second probability distributions being a probability distribution for the building data point at one of the one or more time-steps.

Building system with probabilistic forecasting using a recurrent neural network sequence to sequence model

Methods and systems for training HVAC control using simulated and real experience data

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.

Adaptive training and deployment of single chiller and clustered chiller fault detection models for connected chillers

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.

Growing community of inventors

Santa Clara, CA, United States of America

Sugumar Murugesan