Johnson Controls Tyco IP Holdings Llp

Johnson Controls Technology Company

Tyco Fire + Security Gmbh

Other

University of Utah Research Foundation

Washington University

The University of North Carolina at Chapel Hill

British Columbia Cancer Agency Branch

Nanostring Technologies, Inc.

Matthew J Ellis

Michael J Wenzel

Mohammad N ElBsat

Robert D Turney

Andrew J Przybylski

John H Burroughs

Anas W I Alanqar

Juan Esteban Tapiero Bernal

Brennan H Fentzlaff

Nishith R Patel

Kirk H Drees

Jon T Mueller

Graeme Willmott

Brett M Lenhardt

Timothy C Gamroth

Liming Yang

Alan S Schwegler

Ryan C Beaty

Shawn A Schlagenhaft

Jiaqi Li

Jared W Fread

Masayuki Nonaka

Kerry M Bell

Leyla Mousavi

Bridget E Kapler

Kierstyn R Robbins

Matthew J Asmus

James Byron Burke

Jonathan D Douglas

Charles M Perou

John Ismael Ruiz

Ryan A Baumgartner

Thomas M Seneczko

David S Eidson

Fang Du

Joel S Parker

Mohammad N EIBsat

Serdar Suindykov

Mark L Ziolkowski

Sean M Ferree

Todd Schluechtermann

Victor Zavala Tejeda

Clay G Nesler

Matthew J Deloge

Ranjeet Kumar

Heidi A Hofschulz

Marcus J Fauth

Joseph F Mann

Ganesh Kalpundi

Renee Clair

Anza D'Antonio

Philip S Bernard

Torsten O Nielsen

James Justin Storhoff

Heidi Hofschulz

A controller for performing automated system identification. The controller includes processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations including generating a predictive model to predict system dynamics of a space of a building based on environmental condition inputs and including performing an optimization of a cost function of operating building equipment over a time duration to determine a setpoint for the building equipment. The optimization is performed based on the predictive model. The operations include operating the building equipment based on the setpoint to affect a variable state or condition of the space and include monitoring prediction error metrics over time. The operations include, in response to detecting one of the prediction error metrics exceeds a threshold value, updating the predictive model.

Systems and methods for automated system identification

Systems and methods for reducing health risks with respect to an infectious disease in buildings. Health data for an infectious diseases is used to determine a health risk level for building spaces and individuals in the building. An air handling action or a disinfection action is performed based on the health risk level.

Air quality control and disinfection system

A thermostat for a building zone includes at least one of a model predictive controller and an equipment controller. The model predictive controller is configured to obtain a cost function that accounts for a cost of operating HVAC equipment during each of a plurality of time steps, use a predictive model to predict a temperature of the building zone during each of the plurality of time steps, and generate temperature setpoints for the building zone for each of the plurality of time steps by optimizing the cost function subject to a constraint on the predicted temperature. The equipment controller is configured to receive the temperature setpoints generated by the model predictive controller and drive the temperature of the building zone toward the temperature setpoints during each of the plurality of time steps by operating the HVAC equipment to provide heating or cooling to the building zone.

Smart thermostat with model predictive control

A controller for operating building equipment of a building including processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations including obtaining a first setpoint trajectory from a cloud computation system. The first setpoint trajectory includes setpoints for the building equipment or for a space of the building. The setpoints correspond to time steps of an optimization period. The operations include determining whether a connection between the controller and the cloud computation system is active or inactive at a time step of the optimization period and determining an active setpoint for the time step of the optimization period using either the first or second setpoint trajectory based on whether the connection between the controller and the cloud computation system is active or inactive at the time step. The operations include operating the building equipment based on the active setpoint.

Building control system with features for operating under intermittent connectivity to a cloud computation system

A controller is provided for building equipment including a plurality of devices that operate in parallel to affect an environmental condition of a building. The controller includes one or more processing circuits including one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include lowering an upper bound or raising a lower bound of one or more constraints based on a minimum off schedule or a minimum on schedule for the building equipment, performing an optimization of an objective function subject to the one or more constraints to generate control decisions for the building equipment, and operating the building equipment in accordance with the control decisions to affect the environmental condition of the building.

Control systems and methods for building equipment with optimization modification

One implementation of the present disclosure is a controller for a variable refrigerant flow system. The controller includes processors and memory storing instructions that, when executed by the processors, cause the processors to perform operations including identifying zones within a structure, generating zone groupings defining zone groups and specifying which of the zones are grouped together to form each of the zone groups, generating metric of success values corresponding to the zone groupings and indicating a control feasibility of a corresponding zone grouping, selecting a zone grouping based on the metric of success values, and using the selected zone grouping to operate equipment of the variable refrigerant flow system to provide heating or cooling to the zones.

Variable refrigerant flow system with zone grouping control feasibility estimation

A controller for a system of equipment that operate to produce or consume one or more resources includes one or more processing circuits configured to generate a resource balance constraint using a balance between a first amount of each resource and a second amount of each resource. The first amount of each resource includes at least an amount of the resource produced by the equipment. The second amount of each resource includes at least an amount of the resource consumed by the equipment. The one or more processing circuits are configured to perform a control process using the resource balance constraint to determine amounts of each resource to be produced or consumed by the equipment and operate the equipment to produce or consume the amounts of each resource determined by the control process.

Central plant with asset allocator

A system includes a plurality of thermostats corresponding to a plurality of HVAC systems that serve a plurality of spaces and a computing system communicable with the plurality of thermostats via a network. The computing system is configured to, for each space of the plurality of spaces, obtain a set of training data relating to thermal behavior of the space, identify a model of thermal behavior of the space based on the set of training data, perform a model predictive control process using the model of thermal behavior of the space to obtain a temperature setpoint for the space, and provide the temperature setpoint to the thermostat corresponding to the HVAC system serving the space. The plurality of thermostats are configured to control the plurality of HVAC systems in accordance with the temperature setpoints.

Smart thermostat with model predictive control and demand response integration

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

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

A controller for a building control system includes processors and memory storing instructions that, when executed by the processors, cause the processors to perform operations including identifying zones within a building, analyzing data associated with the zones, and generating zone groupings based on the data associated with the zones. Each of the zone groupings define zone groups and specify which of the zones are grouped together to form each of the zone groups. The operations also include identifying a particular zone grouping from zone groupings based on the data associated with zones and using the particular zone grouping to generate control signals to operate equipment of the building control system to provide heating or cooling to the zones.

Variable refrigerant flow system with zone grouping

Systems and methods for modeling and controlling entities of a building system are provided. An exemplary method includes comparing an input indicating a new entity or connection between entities of a building system to a data model for the building system to determine whether the new entity or connection is represented in the data model, extending the data model to define the new entity or connection in response to determining that the new entity or connection is not represented in the data model, and using the data model with the new entity or connection in a control strategy to generate control decisions for the entities of the building system.

Systems and methods for modeling and controlling building system entities

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 method for controlling equipment includes grouping a plurality of granular assets to form one or more general assets and generating models of the general assets based on the granular assets that form the general assets. Each model corresponds to a general asset and defines a relationship between resources produced by the general asset and resources consumed by the general asset. The method includes performing a first control process using the models of the general assets to determine a first allocation of resources among the general assets. The first allocation defines amounts of the resources to be consumed, produced, or stored by each of the general assets. The method includes operating the equipment to consume, produce, or store the resources in accordance with the first allocation.

Systems and methods for controlling equipment operation using granular asset and general asset models to determine asset allocation

Growing community of inventors

Milwaukee, WI, United States of America

Matthew J Ellis