Johnson Controls Technology Company

Johnson Controls Tyco IP Holdings Llp

Tyco Fire & Security Gmbh

Xilinx, Inc.

Hitachi-johnson Controls Air Conditioning, Inc.

Robert D Turney

Michael J Wenzel

Mohammad N ElBsat

Matthew J Ellis

Nishith R Patel

Kirk H Drees

Liming Yang

Matthew J Asmus

Sudhi R Sinha

Brennan H Fentzlaff

Juan Esteban Tapiero Bernal

Yasutaka Yoshida

Brett M Lenhardt

Paul R Schumacher

Anas W I Alanqar

Jiaqi Li

Masayuki Nonaka

Yunrui Wang

Andrew J Przybylski

Kazumoto Urata

John H Burroughs

Koji Naito

Timothy C Gamroth

Alan S Schwegler

Peter A Craig

Michael Risbeck

Fang Du

Christos T Maravelias

Kerry M Bell

Serdar Suindykov

Leyla Mousavi

Jingduo Fan

Bridget E Kapler

Ali M Reza

Kierstyn R Robbins

Zhizhong Pang

Henry O Marcy, V

Youngchoon Park

Timothy I Salsbury

Prasanna Sundararajan

Curtis Christian Crane

John M House

Homero L Noboa

Kornelis Antonius Vissers

Graeme Willmott

James P Kummer

James Byron Burke

Brandon Blodget

Justin Patrick Kauffman

Jonathan D Douglas

Wael Badawy

Justin J Seifi

Matthew T Trawicki

Ryan A Baumgartner

Garrett Wiens-Kind

Thomas M Seneczko

Carol T Tumey

Dennis J Flood

David S Eidson

Mark Paluszkiewicz

Wilson Chung

Hayato Mori

Zhigang Wu

Nicole A Madison

Yoshiko Watanabe

Michael F Jaeger

Michael H Zamalis

Toader-Adrian Chirila-Rus

Tricia J Valentine

Collin W Eggert

Mark L Ziolkowski

Clay G Nesler

Matthew J Deloge

Daniel J Brandt

Heidi A Hofschulz

Zijian Cao

Ihab Amer

Shaoyan Pan

Mori Hayato

Joseph F Mann

Ganesh Kalpundi

Renee Clair

Anza D'Antonio

Farhad Balali

Nicolaus P Meyer

Ruoyu Chen

A model predictive maintenance (MPM) system for building equipment includes an operational cost predictor configured to predict a cost of operating the building equipment over a duration of an optimization period, a maintenance cost predictor configured to predict a cost of performing maintenance on the building equipment over the duration of the optimization period, and an objective function optimizer configured to optimize an objective function to predict a total cost associated with the building equipment over the duration of the optimization period. The objective function includes the predicted cost of operating the building equipment and the predicted cost of performing maintenance on the building equipment. The MPM system includes an equipment controller configured to operate the building equipment to affect a variable state or condition in a building in accordance with values of one or more decision variables obtained by optimizing the objective function.

Model predictive maintenance system for building equipment

A predictive controller for a building energy system obtains a function that defines an amount of energy consumed by both waterside equipment and airside equipment during a time period as a summation of multiple equipment-specific energy components including a waterside energy component indicating an amount of energy consumed by the waterside equipment during the time period and an airside energy component indicating an amount of energy consumed by the airside equipment during the time period. The predictive controller performs a predictive control process using the function to determine values of the equipment-specific energy components during the time period. The predictive control process includes predicting the amount of energy consumed by both the waterside equipment and the airside equipment during the time period based on the values of the equipment-specific energy components. The predictive controller operates the waterside equipment and the airside equipment using the values of the equipment-specific energy components.

Building energy system with predictive control of waterside and airside HVAC equipment

A method for controlling HVAC equipment includes using a dynamic thermal model that describes heat transfer characteristics of a building to predict a temperature of the building as a function of control inputs to HVAC equipment that operate to affect the temperature of the building. The method includes obtaining time-varying utility prices and a predicted heating or cooling load for the building and performing a predictive control process using the dynamic thermal model, the time-varying utility prices, and the predicted heating or cooling load to generate the control inputs to the HVAC equipment subject to constraints on the temperature of the building.

Systems and methods for cascaded model predictive control

A model predictive maintenance (MPM) system for building equipment includes one or more processing circuits having 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 including estimating a degradation state of the building equipment, using a degradation impact model to predict an amount of one or more input resources consumed by the building equipment to produce one or more output resources based on the degradation state of the building equipment, generating a maintenance schedule for the building equipment based on the amount of the one or more input resources predicted by the degradation impact model, and initiating a maintenance activity for the building equipment in accordance with the maintenance schedule.

Model predictive maintenance system with degradation impact model

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 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 model predictive maintenance (MPM) system for building equipment includes one or more processing circuits including one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include obtaining one or more performance indicators for the building equipment and determining whether a trigger condition has been satisfied based on the one or more performance indicators. The operations include triggering a model predictive maintenance process to generate a maintenance schedule for the building equipment in response to determining that the trigger condition has been satisfied. The operations include initiating a maintenance activity for the building equipment in accordance with the maintenance schedule.

Model predictive maintenance system with event or condition based performance

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 equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.

High level central plant optimization

An energy plant includes a plurality of subplants, a high level optimizer, a low level optimizer, and a controller. The plurality of subplants include a cogeneration subplant configured to generate steam and electricity and a chiller subplant electrically coupled to the cogeneration subplant and configured to consume the electricity generated by the cogeneration subplant. The high level optimizer is configured to determine recommended subplant loads for each of the plurality of subplants. The recommended subplant loads include a rate of steam production and a rate of electricity production of the cogeneration subplant and a rate of electricity consumption of the chiller subplant. The low level optimizer is configured to determine recommended equipment setpoints for equipment of the plurality of subplants based on the recommended subplant loads. The controller is configured to operate the equipment of the plurality of subplants based on the recommended equipment setpoints.

Systems and methods for monitoring and controlling an energy plant

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 predictive controller for building equipment associated with a building includes one or more processing circuits configured to control electric energy used by the building equipment. The building equipment includes an electric energy using component. The one or more processing circuits are configured to utilize a predictive cost function to determine a first amount of the electric energy supplied from an energy grid source and a second amount of the electric energy supplied from a second energy source to the electric energy using component.

Building equipment with predictive control and allocation of energy from multiple energy sources

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 controller for heating, ventilation, or air conditioning (HVAC) equipment including a processing circuit configured to perform a first optimization to generate a first set of control decisions for HVAC equipment including waterside HVAC equipment that consume resources from utility providers to generate a heated or chilled fluid and airside HVAC equipment that receive and use the fluid from the waterside HVAC equipment to heat or cool a supply of airflow for a building. The processing circuit is configured to perform a second optimization subject to a constraint based on a result of the first optimization to generate a second set of control decisions for the HVAC equipment and to combine the first and second sets to generate a combined set of control decisions for the HVAC equipment. The processing circuit is configured to operate the HVAC equipment in accordance with the combined set of control decisions.

HVAC controller with predictive cost optimization

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

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Watertown, WI, United States of America

Robert D Turney