Johnson Controls Tyco IP Holdings Llp

Johnson Controls Technology Company

Nishith R Patel

Robert D Turney

Matthew J Ellis

Michael J Wenzel

Brett M Lenhardt

A predictive controller for a building energy system includes one or more processing circuits configured to obtain a constraint that defines a total electric load to be served by the building energy system at each time step of a time period as a summation of multiple source-specific energy components. The source-specific energy components include a first energy component indicating a first amount of energy to obtain from a first energy source during the time step and a second energy component indicating a second amount of energy to obtain from a second energy source during the time step. The one or more processing circuits are configured to perform a predictive control process subject to the constraint to determine values of the source-specific energy components at each time step of the time period and operate equipment of the building energy system using the values of the source-specific energy components.

Building energy system with predictive control of battery and green energy resources

A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.

Building HVAC system with multi-level model predictive control

A heating, ventilation, or air conditioning (HVAC) system for a building includes airside HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers are configured to generate airside energy targets for the one or more building spaces using a heat transfer model that defines a relationship between the airside energy targets, a temperature of the one or more building spaces, and a thermal capacitance of the one or more building spaces. The one or more controllers are configured to control the airside HVAC equipment in accordance with the airside energy targets.

HVAC system with predictive airside control

A building energy system includes HVAC equipment, green energy generation, a battery, and a predictive controller. The HVAC equipment provide heating or cooling for a building. The green energy generation collect green energy from a green energy source. The battery stores electric energy including at least a portion of the green energy provided by the green energy generation and grid energy purchased from an energy grid and discharges the stored electric energy for use in powering the HVAC equipment. The predictive controller generates a constraint that defines a total energy consumption of the HVAC equipment at each time step of an optimization period as a summation of multiple source-specific energy components and optimizes the predictive cost function subject to the constraint to determine values for each of the source-specific energy components at each time step of the optimization period.

A variable refrigerant flow (VRF) system for a building includes an outdoor VRF unit, a plurality of indoor VRF units, a battery, and a predictive VRF controller. The outdoor VRF unit includes powered VRF components configured to apply heating or cooling to a refrigerant. The indoor VRF units are configured to use the heated or cooled refrigerant to provide heating or cooling to a plurality of building zones. The battery is configured to store electric energy and discharge the stored electric energy for use in powering the powered VRF components. The predictive VRF controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from an energy grid and an optimal amount of electric energy to store in the battery or discharge from the battery for use in powering the powered VRF components at each time step of an optimization period.

Variable refrigerant flow system with predictive control

A central energy facility (CEF) includes a plurality of powered CEF components, a battery unit, and a predictive CEF controller. The powered CEF components include a chiller unit and a cooling tower. The battery unit is configured to store electric energy from an energy grid and discharge the stored electric energy for use in powering the powered CEF components. The predictive CEF controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from the energy grid and an optimal amount of electric energy to store in the battery unit or discharge from the battery unit for use in powering the powered CEF components at each time step of an optimization period.

Building equipment with predictive control

An air handling unit (AHU) or rooftop unit (RTU) or other building device in a building includes one or more powered components and is used with a battery, and a predictive controller The battery is configured to store electric energy and discharge the stored electric energy for use in powering the powered components. The predictive controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from an energy grid and an optimal amount of electric energy to store in the battery or discharge from the battery for use in powering the powered components at each time step of an optimization period.

Air handling unit and rooftop unit with predictive control

A building HVAC system includes an airside system having a plurality of airside subsystems, a high-level model predictive controller (MPC), and a plurality of low-level airside MPCs. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to the airside subsystem. The high-level MPC is configured to perform a high-level optimization to generate an optimal airside subsystem load profile for each airside subsystem. The optimal airside subsystem load profiles optimize energy cost. Each of the low-level airside MPCs corresponds to one of the airside subsystems and is configured to perform a low-level optimization to generate optimal airside temperature setpoints for the corresponding airside subsystem using the optimal airside subsystem load profile for the corresponding airside subsystem. Each of the low-level airside MPCs is configured to use the optimal airside temperature setpoints for the corresponding airside subsystem to operate the airside HVAC equipment of the corresponding airside subsystem.

HVAC system using model predictive control with distributed low-level airside optimization

A heating, ventilation, or air conditioning (HVAC) system for a building includes indoor subsystems, a high-level controller, and low-level controllers. Each indoor subsystem includes one or more indoor units configured to provide heating or cooling to one or more building spaces. The high-level controller generates a plurality of indoor subsystem energy targets, each indoor subsystem energy target corresponding to one of the plurality of indoor subsystems and generated based on a thermal capacitance of one or more building spaces to which heating or cooling is provided by the corresponding indoor subsystem. Each low-level indoor controller corresponds to one of the indoor subsystems and generates indoor setpoints for the one or more indoor units of the corresponding indoor subsystem using the indoor subsystem energy target for the corresponding indoor subsystem and operates the one or more indoor units of the corresponding indoor subsystem using the indoor setpoints.

A building HVAC system includes an airside system having a plurality of airside subsystems, a high-level controller, and a plurality of low-level airside controllers. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to one or more building spaces. The high-level controller is configured to generate a plurality of airside subsystem energy targets, each airside subsystem energy target corresponding to one of the plurality of airside subsystems and generated based on a thermal capacitance of the one or more building spaces to which heating or cooling is provided by the corresponding airside subsystem. Each low-level airside controller corresponds to one of the airside subsystems and is configured to control the airside HVAC equipment of the corresponding airside subsystem in accordance with the airside subsystem energy target for the corresponding airside subsystem.

HVAC system using model predictive control with distributed low-level airside optimization and airside power consumption model

An air handling unit (AHU) or rooftop unit (RTU) in a building HVAC system includes one or more powered components, a battery, and a predictive controller. The powered components include a fan configured to generate a supply airstream provided to one or more building zones. The battery is configured to store electric energy and discharge the stored electric energy for use in powering the powered components. The predictive controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from an energy grid and an optimal amount of electric energy to store in the battery or discharge from the battery for use in powering the powered components at each time step of an optimization period.

A model predictive control system is used to optimize energy cost in a variable refrigerant flow (VRF) system. The VRF system includes an outdoor subsystem and a plurality of indoor subsystems. The model predictive control system includes a high-level model predictive controller (MPC) and a plurality of low-level indoor MPCs. The high-level MPC performs a high-level optimization to generate an optimal indoor subsystem load profile for each of the plurality of indoor subsystems. The optimal indoor subsystem load profiles optimize energy cost. Each of the low-level indoor MPCs performs a low-level optimization to generate optimal indoor setpoints for one or more indoor VRF units of the corresponding indoor subsystem. The indoor setpoints can include temperature setpoints and/or refrigerant flow setpoints for the indoor VRF units.

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Snellville, GA, United States of America

Nishith R Patel