Johnson Controls Technology Company

Johnson Controls Tyco IP Holdings Llp

Tyco Fire & Security Gmbh

Xilinx, Inc.

Hitachi-johnson Controls Air Conditioning, Inc.

Milwaukee Electric Tool Corporation

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

Yasutaka Yoshida

Juan Esteban Tapiero Bernal

Brett M Lenhardt

Paul R Schumacher

Anas W I Alanqar

Jiaqi Li

Masayuki Nonaka

Jingduo Fan

Yunrui Wang

Youngchoon Park

Andrew J Przybylski

Kazumoto Urata

Koji Naito

Timothy C Gamroth

John H Burroughs

Alan S Schwegler

Peter A Craig

Michael Risbeck

Fang Du

Hayato Mori

Zhigang Wu

Christos T Maravelias

Kerry M Bell

Leyla Mousavi

Serdar Suindykov

Kierstyn R Robbins

Henry O Marcy, V

Bridget E Kapler

Ali M Reza

Zhizhong Pang

Timothy Ryan Obermann

Timothy I Salsbury

Prasanna Sundararajan

Curtis Christian Crane

John M House

Homero L Noboa

Graeme Willmott

Kornelis Antonius Vissers

James P Kummer

James Byron Burke

Brandon Blodget

Justin Patrick Kauffman

Ian A Duncan

Douglas R Fieldbinder

Jonathan D Douglas

Wael Badawy

Justin J Seifi

Matthew T Trawicki

Garrett Wiens-Kind

Ryan A Baumgartner

Thomas M Seneczko

Carol T Tumey

Dennis J Flood

Mark Paluszkiewicz

David S Eidson

Nicole A Madison

Wilson Chung

Michael F Jaeger

Yoshiko Watanabe

Michael H Zamalis

Toader-Adrian Chirila-Rus

Mark L Ziolkowski

Collin W Eggert

Tricia J Valentine

Clay G Nesler

Matthew J Deloge

Zijian Cao

Shaoyan Pan

Farhad Balali

Anza D'Antonio

Daniel J Brandt

Renee Clair

Bo Fan

Ganesh Kalpundi

Joseph F Mann

Joshua D Carl

Zhongyue Sun

Nicolaus P Meyer

Mori Hayato

Ihab Amer

Ruoyu Chen

Heidi A Hofschulz

Michael J Risbeck

Heidi Hofschulz

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 building optimization system includes charging and discharging a battery of a battery power vehicle. The building optimization system includes a charging system configured to cause the battery of the battery powered vehicle to charge or discharge. The building optimization system also includes an optimization controller including a processing circuit. The processing circuit is configured to receive charging constraints for the battery powered vehicle, determine whether to charge discharge the battery of the battery powered vehicle based on the charging constraints, and cause the charging system to charge or discharge the battery of the battery powered vehicle based on the optimization.

Building energy optimization system with battery powered vehicle cost optimization

A method for detecting faults in a building management system (BMS) is shown. The method includes receiving time series data characterizing an operating performance of one or more BMS devices. The method further includes processing the time series data using multiple different fault detection methods to generate multiple fault detection results. The method includes providing the multiple fault detection results as outputs from the multiple different fault detection methods. The method includes applying the multiple fault detection results as inputs to a neural network that determines whether the multiple fault detection results are indicative of a fault condition in the BMS.

Building management system with supervisory fault detection layer

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

A power tool including a housing, an input, a gear train, a motor at least partially supported by the housing and coupled to the gear train, and a controller including a processor and a memory. The controller is coupled to the input and the motor and is configured to receive an input signal from the input, determine an operating direction and an operating speed based on the input signal, cause the motor to rotate the gear train in a direction opposite the operating direction for a predetermined amount of rotation, and cause the motor to rotate in the operating direction at the operating speed.

Maximum power tool startup torque

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

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Watertown, WI, United States of America

Robert D Turney