Johnson Controls Tyco IP Holdings Llp

Johnson Controls Technology Company

Hitachi-johnson Controls Air Conditioning, Inc.

Tyco Fire & Security Gmbh

Liming Yang

Robert D Turney

Michael J Wenzel

Yasutaka Yoshida

Mohammad N ElBsat

Curtis Christian Crane

Yunrui Wang

Matthew J Ellis

Kazumoto Urata

Justin Patrick Kauffman

Thomas M Seneczko

Masayuki Nonaka

Zhigang Wu

Timothy I Salsbury

Matthew J Asmus

John M House

Koji Naito

Hayato Mori

Zhongyue Sun

Bo Fan

Ruoyu Chen

A refrigerant charge controller for heating, ventilation, or air conditioning (HVAC) equipment includes a processing circuit configured to analyze usage data for the HVAC equipment using a machine learning model to estimate an amount of refrigerant used by the HVAC equipment, identify a refrigerant deficiency based on the amount of refrigerant, and initiate a corrective action in response to identifying the refrigerant deficiency.

Systems and methods for controlling variable refrigerant flow systems using artificial intelligence

A method of operating a chiller to avoid future surge events, the method comprises applying chiller operating data associated with a chiller as an input to one or more machine learning models; and generating a threshold for a controllable chiller variable to prevent a future chiller surge event from occurring based on an output of the one or more machine learning models, further comprising affecting operation of the chiller based on the threshold to prevent the future chiller surge event from occurring. The method enables automatic control of a chiller to avoid future chiller surge events.

Building system with automatic chiller anti-surge control

An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.

Systems and methods for maintaining occupant comfort for various environmental conditions

An automatic work order generation system for model predictive maintenance (MPM) of building equipment including an MPM system including an equipment controller to operate the building equipment to affect an environmental condition of a building. The MPM system can perform a predictive optimization to determine a service time at which to service the building equipment. The automatic work order generation system includes an equipment service scheduler that can determine whether any service providers are available to perform equipment service within a predetermined time range of the service time. In response to determining that service providers are available to perform the equipment service, the equipment service scheduler can select a service provider and an appointment time based on one or more service provider attributes. The equipment service scheduler can generate a service work order and transmit the service work order to the service provider to schedule a service appointment.

Model predictive maintenance system with automatic service work order generation

A method of operating a chiller includes applying chiller operating data associated with the chiller as an input to one or more machine learning models, generating a boundary for a controllable chiller variable based on an output of the one or more machine learning models, and affecting operation of the chiller based on the boundary.

A building cooling system includes one or more cooling devices operable to affect an indoor air temperature of a building and a system management circuit. The system management circuit is configured to obtain an objective function that includes a power consumption term and a comfort term, perform an optimization of the objective function over a time horizon to determine values of the cooling capacity of the cooling devices where each value of the cooling capacity corresponds to a time step of the time horizon, and control the cooling devices based on the values of the cooling capacity of the cooling devices. The comfort term of the objective function a difference between a prediction of the indoor air temperature of the building and a temperature setpoint for the building, while the power consumption term is a function of the power consumption of the one or more cooling devices.

Building cooling systems with energy optimization and model predictive control

A predictive heating system for a building zone includes building equipment, a temperature sensor, a humidity sensor, and a predictive heating controller. The building equipment is operable to affect an environmental condition of the building zone in a heating mode of operation and a cooling mode of operation. The temperature sensor is configured to measure a temperature of the building zone. The humidity sensor is configured to measure humidify of the building zone. The predictive heating controller is configured to predict an occupancy time of the building zone over a future time period, determine a dehumidification time period before the occupancy time of the building zone, determine a heating time period before the occupancy time of the building zone, operate the building equipment to dehumidify the building zone over the dehumidification time period, and operate the building equipment to heat the building zone over the heating time period.

Building HVAC system with predictive temperature and humidity control

A refrigerant cycle of the present invention includes including an outdoor unit, a plurality of indoor units, and a controller for controlling the refrigeration cycle using at least one EEV. The controller comprises a velocity PID component executing a velocity PID control using pulse number for driving an EEV, three-state controller determining a driving state of the EEV and generating pulse commands designating the driving state, and a state machine driving the EEV depending on the pulse commands generated by the three-state controller.

Refrigeration cycle control

A refrigeration cycle including at least one outdoor unit including a plurality of compressors and indoor units each placed in indoor spaces comprises a plurality of compressors for supplying refrigerant to indoor units; and a controller for controlling cooperatively a plurality of the compressors in the outdoor unit to provide a capacity for air-conditioning in the indoor spaces through the indoor units, wherein the controller controls operation of the compressors so as to minimize a cost including start/stop of each compressor by prediction of an air-conditioning requirement in a next time chunk.

Refrigeration cycle optimization

A method for controlling a variable refrigerant flow (VRF) system includes applying a time window to sensor data associated with the VRF system, the sensor data including input data points and having a first resolution, wherein applying the time window to the sensor data isolates a subset of the input data points; applying a timing weight to one or more input data points in the subset of the input data points to generate corrected data points having a second resolution higher than the first resolution; creating a virtual sensor and mapping the corrected data points to an output of the virtual sensor; and controlling the VRF system based on an output of the virtual sensor. The use of virtual sensors with a higher resolution than corresponding physical sensors in this manner allows for existing physical sensors to be used while improving performance of the VRF system.

Variable refrigerant flow system with automatic sensor data correction

A refrigeration cycle, an air conditioning system, and a method for controlling a refrigeration cycle are provided. The refrigeration cycle includes an outdoor unit, an indoor unit, a controller, and an inverter. The controller controls a compressor and an outdoor fan of the air conditioning system so as to minimize a total electric power consumption of the air conditioning system. The inverter controls the outdoor fan in a rotation state predicted from a capacity demand in an air conditioning space depending on an operation mode and sensor values. The controller predicts the capacity demand and controls a rotation rate of the outdoor fan based on a prediction of the capacity demand.

Predictive refrigeration cycle

A variable refrigerant flow (VRF) system for a building includes a plurality of outdoor VRF units configured to heat or cool a refrigerant for use in heating or cooling the building and an extremum-seeking controller. The extremum-seeking controller is configured to determine a total power consumption of the plurality of outdoor VRF units, generate a pressure setpoint for the plurality of outdoor VRF units using an extremum-seeking control technique that drives the total power consumption toward an extremum, and use the pressure setpoint to operate the plurality of outdoor VRF units.

Variable refrigerant flow system with pressure optimization using extremum-seeking control

A variable refrigerant flow system includes one or more outdoor units and a first indoor unit of a plurality of indoor units configured to receive refrigerant from the one or more outdoor units. The first indoor unit is configured to serve a first building zone. The variable refrigerant flow system also includes a user input device configured to receive a user command requesting heating or cooling of the first building zone by the first indoor unit. The variable refrigerant flow system also includes a controller configured to receive the command from the user input device, receive an indication of a current price of energy, in response to receiving the command generate a constraint on a capacity of the one or more outdoor units based on the current price of energy, and control the one or more outdoor units to operate in accordance with the constraint.

Variable refrigerant flow system with capacity limits

A variable refrigerant flow (VRF) system for a building. The VRF system includes at least one outdoor VRF unit configured to heat or cool a refrigerant for use in heating or cooling the building. The at least one outdoor VRF unit includes a sub-cooler and a bypass expansion valve configured to control a flow of the refrigerant through the sub-cooler and an extremum-seeking controller configured to generate a sub-cooling temperature setpoint for the at least one outdoor VRF unit. The extremum-seeking controller is configured to determine a total power consumption of the at least one outdoor VRF unit, generate a sub-cooling temperature setpoint for the at least one outdoor VRF unit using an extremum-seeking control technique that drives the total power consumption toward an extremum, and use the sub-cooling temperature setpoint to operate the at least one outdoor VRF unit.

Growing community of inventors

Mequon, WI, United States of America

Liming Yang