Johnson Controls Technology Company

Johnson Controls Tyco IP Holdings Llp

Tyco Fire & Security Gmbh

Con Edison Battery Storage, LLC

Taurus Des, LLC

Hitachi-johnson Controls Air Conditioning, Inc.

Johnson Controls Technology LLC

Michael J Wenzel

Mohammad N ElBsat

Kirk H Drees

Robert D Turney

Matthew J Ellis

Andrew J Przybylski

John H Burroughs

Anas W I Alanqar

Matthew J Asmus

Brett M Lenhardt

James P Kummer

Juan Esteban Tapiero Bernal

Graeme Willmott

Brennan H Fentzlaff

Ryan A Baumgartner

Fang Du

Liming Yang

Andrew J Boettcher

Jon T Mueller

Mohammad N EIBsat

Jingduo Fan

Vineet Binodshanker Sinha

James Byron Burke

Steven R Vitullo

Timothy C Gamroth

Shawn D Schubert

Radu M Dorneanu

Peter A Craig

Nishith R Patel

Ryan C Beaty

Yasutaka Yoshida

Shawn A Schlagenhaft

Nicole Ann Madison

Thomas M Seneczko

Masayuki Nonaka

Carol T Tumey

David S Eidson

Sudhi R Sinha

Homero L Noboa

Alan S Schwegler

Jiaqi Li

Abhishek Gupta

Michael James Risbeck

Jared W Fread

Payal Pancholi

Kerry M Bell

Serdar Suindykov

Leyla Mousavi

Tricia J Valentine

Collin W Eggert

Bridget E Kapler

Kierstyn R Robbins

Koji Naito

Young M Lee

Rajiv Ramanasankaran

Jonathan D Douglas

Michael Risbeck

Lisa E Strand

John Ismael Ruiz

Camille M Aucoin

Dennis J Flood

Joseph P Carmody

Hayato Mori

Zhigang Wu

Yoshiko Watanabe

Christos T Maravelias

Joseph C Jones

Mark L Ziolkowski

Todd Schluechtermann

Apoorva Gupta

Clay G Nesler

Matthew J Deloge

Peter A Kinsella

Zhizhong Pang

Victor Zavala Tejeda

Mori Hayato

Nicolaus P Meyer

Yaqing Wu

Parisa Khosravi

Joshua D Carl

Joseph F Mann

Ganesh Kalpundi

Renee Clair

Anza D'Antonio

Farhad Balali

Ranjeet Kumar

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 method for controlling building equipment includes providing a user interface comprising a graphical representation of a relationship between a carbon emissions control objective and a second control objective that competes with the carbon emissions control objective over a range of control strategies for the building equipment, and assigning a weight to the carbon emissions control objective or the second control objective in an objective function. The weight is associated with a control strategy that corresponds to a user selection based on the graphical representation. The method also includes generating control decisions for the building equipment using the objective function with the weight assigned to the carbon emissions control objective or the second control objective and operating the building equipment in accordance with the control decisions.

Building control system with multi-objective control of carbon emissions and occupant comfort

An energy optimization system includes an asset sizing system to determine an asset size and to cause a new asset to be purchased or installed. The energy optimization system uses a cost function dependent on a cost of operating a building system and on an initial purchase price of the new asset. The new asset may be a heat recover chiller, a solar energy system, a hydroelectric system, or an energy storage system. The optimization may be performed subject to a constraint related to the use of a resource supplied to the building system by one or more utilities. The optimization may include minimizing a simple pay back period or to maximizing a rate of return on the purchase of the new asset.

Asset sizing with utility use constraints

Systems and methods for controlling building equipment include simulating operation of the building equipment at a plurality of initial points to generate corresponding values of a first control objective and a second control objective that competes with the first control objective, automatically generating a new point at which to run a new simulation based on the plurality of initial points and the corresponding values of the control objectives, running the new simulation at the new point to generate corresponding values of the control objectives, classifying a subset of the plurality of initial points and the new point as Pareto-optimal points based on the corresponding values of the control objectives, and operating the building equipment at a Pareto-optimal point selected from the subset of the plurality of initial points and the new point classified as Pareto-optimal points.

Systems and methods for assessing and controlling sustainability of an energy plant

A frequency response optimization system includes a battery, a power inverter, and a frequency response controller. The battery is configured to store and discharge electric power. The power inverter is configured to control an amount of the electric power stored or discharged from the battery. The frequency response controller includes a high level controller configured to determine values of clipping parameters and a low level controller configured to use the values of the clipping parameters to modify battery power setpoints. The power inverter is configured to use the modified battery power setpoints to control the amount of the electric power stored or discharged from the battery.

Frequency response control system with clipping parameter determination

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 method includes providing a net consumption trajectory comprising net consumption targets for one or more subperiods of a time period. Each net consumption target indicates a target difference from a beginning of a time period to an end of the subperiod between total consumption and total production or offset. The method also includes generating, for a subperiod of the plurality of subperiods, a set of curtailment actions predicted to achieve the net consumption target for the subperiod and implementing the set of curtailment actions.

Generating set of curtailment actions predicted to achieve net consumption target for a subperiod

A method includes providing a control algorithm that may include a constraint constraining production of the equipment below maximum values for a plurality of time steps, and dynamically adjusting the constraint by updating the maximum values as a function of predicted values of a dynamic variable for the plurality of time step. The dynamic variable affects an actual maximum production of the equipment. The method includes determining control decisions for the plurality of time steps by executing the control algorithm and controlling the equipment in accordance with the control decisions.

Control system for building equipment with dynamic capacity constraints

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

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 system includes a first subsystem configured to produce a resource by consuming electricity, a second subsystem configured to produce the resource by consuming a fuel, and a controller. The controller is configured to determine an allocation of a predicted demand for the resource over a future time period between the first subsystem and the second subsystem based on a first carbon emissions rate associated with off-site production of the electricity and a second carbon emissions rate associated with on-site consumption of the fuel. The controller is also configured to control the first subsystem and the second subsystem to produce the resource in accordance with the allocation during the future time period.

Control system with multi-factor carbon emissions optimization

A building system that operates to receive a plurality of data samples from building equipment of the building and identity whether one or more data samples of the plurality of data samples are anomalous indicating that one or more pieces of the building equipment are possibly experiencing a fault. The building system operates to generate one or more data health scores for the building equipment based on whether the one or more data samples are anomalous, the one or more data health scores indicating quality levels of the plurality of data samples and generate user interface data configured to cause a user device to display a user interface providing indications of the one or more pieces of building equipment that are possibly experiencing the fault and the one or more data health scores.

Building system for building equipment with fault based data health operations

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

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Oak Creek, WI, United States of America

Michael J Wenzel