Johnson Controls Tyco IP Holdings Llp

Johnson Controls Technology Company

Viswanath Ramamurti

Young Min Lee

Sugumar Murugesan

Kirk H Drees

Michael Risbeck

Zhanhong Jiang

Youngchoon Park

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

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

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

A method for generating a human-like response to a voice or text command includes receiving an input sequence of words and processing the input sequence of words to generate a trigger word that is indicative of a desired nature of the human-like response. The method further includes encoding a neural network using the trigger word and generating the human-like response using an output of the neural network. The method enables implementation of voice command functionality in various types of devices with only a small amount of training data.

Systems and methods for using trigger words to generate human-like responses in virtual assistants

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 management autonomous HVAC control using reinforcement learning with occupant feedback

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: receive utterance data from a voice assist device; determine a location of the voice assist device; analyze the utterance data to identify a sentiment relating to a temperature of the location; and control an HVAC system to adjust the temperature of the location based on the sentiment.

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San Leandro, CA, United States of America

Viswanath Ramamurti