Johnson Controls Tyco IP Holdings Llp

Johnson Controls Technology Company

Tyco Fire + Security Gmbh

John H Burroughs

Michael J Wenzel

Andrew J Przybylski

Matthew J Ellis

Mohammad N ElBsat

Kirk H Drees

Graeme Willmott

Anas W I Alanqar

Robert D Turney

Matthew J Asmus

Jon T Mueller

Juan Esteban Tapiero Bernal

John Ismael Ruiz

Ryan A Baumgartner

Mohammad N EIBsat

Peter J Hazelberg

Todd Schluechtermann

Joseph C Jones

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

A brokering architecture is disclosed. The brokering architecture focuses on storing changes to the building system. Measurements are quantized to an integer representation and encoded so that time series data is stored in a series of changes of value. The encoded timeseries may be compressed using compression schemes that advantageously make use of small changes between adjacent stored values of the timeseries. Compressed timeseries values allow more data to be stored in RAM or other short-term storage and provide a more responsive system to control physical systems like buildings. Timeseries compression may be provided through extensions accessed through a structured data format on the message bus. Extensions can cause the execution of certain instructions based on the fields in the structured data and may be used to provide functionality in addition to timeseries compression including BACnet communications, predictions, and simulations.

Building system with brokering architecture to provide timeseries compression and extensions

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

Systems and methods for automatically commissioning and operating a heating, ventilation, or air conditioning (HVAC) system for a building site are provided. An exemplary method includes constructing a model using physical equipment of the HVAC system and relationships between the physical equipment. The model indicates connections between the physical equipment and one or more resources produced or consumed by the physical equipment. The method includes generating a mapping between points of the physical equipment at the building site and corresponding variables of the model, using the model to generate values of one or more control variables of the model, and operating the physical equipment by providing the values of the control variables to corresponding points of the physical equipment based on the mapping.

Building management system with control framework

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

A controller includes a processing circuit comprising 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 determining a gradient of an objective function with respect to a constraint variable used to define a constraint on a control process that uses the objective function and modifying the constraint variable to have a modified value in response to determining that a trigger condition is satisfied. The trigger condition is based on the gradient. The operations include performing the control process subject to the constraint using the modified value of the constraint variable and operating equipment in accordance with a result of the control process.

Building control system with load curtailment optimization

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

A method for controlling production of one or more refined resources by an energy provider includes predicting a demand for the refined resources by one or more consumers of the refined resources as a function of an incentive offered by the energy provider. The method further includes performing an optimization of an objective function subject to a constraint based on the predicted demand for the refined resources to determine an amount of the refined resources for the energy provider to produce and a value of the incentive at multiple times within a time period. The method also includes providing setpoints for equipment of the energy provider that cause the equipment to produce the amount of the refined resources determined by performing the optimization.

Energy control system with energy provider level demand optimization

A method for controlling an energy production and distribution system includes identifying sources that supply input resources, subplants that produce output resources using the input resources, and sinks that consume the output resources. The method includes obtaining a cost function including a cost of producing the output resources and generating a transit time constraint that requires the input resources be sent from the sources to the subplants at a first departure time that occurs before a first arrival time at which the input resources are predicted to be used by the subplants. The method includes solving an optimization problem to determine an amount of the output resources to produce at each of multiple time steps within a time period. Solving the optimization problem includes performing an optimization of the cost function subject to the transit time constraint.

Control system for generating and distributing energy resources and operating building equipment accounting for resource provider contraints

A controller for a plurality of heating, ventilation, or air conditioning (HVAC) devices includes a processing circuit that includes one or more processors and memory. The controller detects a change in condition that affects an operating status of a first HVAC device of the plurality of HVAC devices. The controller uses schematic relationships between the plurality of HVAC devices to determine a reduced subset of the plurality of HVAC devices for which operating parameters are to be generated based on the operating status of the first HVAC device. The controller generates operating parameters for the reduced subset of the plurality of HVAC devices and operates the plurality of HVAC devices using the operating parameters.

Central plant control system with dynamic computation reduction

A method for automatically commissioning and operating an HVAC system to serve energy loads of a building site is shown. The method includes querying site information describing the building site to identify physical equipment and relationships between the physical equipment. The method further includes constructing an asset allocator model, the asset allocator model indicating connections between the physical equipment and resources produced or consumed by the physical equipment. The method further includes generating a mapping between points of the physical equipment at the building site and corresponding variables of the asset allocator model. The method further includes using the asset allocator model to generate values of one or more control variables of the asset allocator model. The method further includes adjusting an operation of the physical equipment by triggering software elements to automatically push updated values of the control variables to corresponding points of the physical equipment.

A controller for operating building equipment of a building. The controller includes one or more processors. The controller includes one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include comparing one or more model parameters of predictive models describing zones of the building to determine one or more zone groups for the building. The operations include generating one or more zone group models corresponding to the one or more zone groups. The operations include operating the building equipment using the one or more zone group models to affect a variable state or condition of the building.

Building control system with zone grouping based on predictive models

A system controlling equipment to serve energy loads of a building includes a cost function generator configured to obtain a cost function of decision variables representing an amount of resources consumed or produced by the equipment, an optimizer configured to perform a first optimization of the cost function subject to a first set of constraints defining first values of constraint variables to generate a first result defining first values of decision variables, a constraint modifier configured determine a cost gradient, recommend changes to constraint variables, and modify constraint variables to modified values in response to changes. The optimizer is configured to perform an optimization of the cost function subject to a second set of constraints to generate a second optimization result defining second values of the decision variables. The system includes a controller that operates equipment to consume or produce resources defined by second values of the decision variables.

A control system for a central energy facility with distributed energy storage includes a high level coordinator, a low level airside controller, a central plant controller, and a battery controller. The high level coordinator is configured to perform a high level optimization to generate an airside load profile for an airside system, a subplant load profile for a central plant, and a battery power profile for a battery. The low level airside controller is configured to use the airside load profile to operate airside HVAC equipment of the airside subsystem. The central plant controller is configured to use the subplant load profile to operate central plant equipment of the central plant. The battery controller is configured to use the battery power profile to control an amount of electric energy stored in the battery or discharged from the battery at each of a plurality of time steps in an optimization period.

Control system for central energy facility with distributed energy storage

A method for operating equipment according to sequence of operation using geometric models including obtaining a first geometric model for a first set of equipment and a second geometric model for a second set of equipment, the first set of equipment and the second set of equipment defined by the sequence of operation for the equipment, locating, on the first geometric model, a first nearest operating point based on a desired operating point, generating a first modified geometric model by removing one or more operating points that do not satisfy the first nearest operating point, generating a merged geometric model by merging the first modified geometric model with the second geometric model, locating a second nearest operating point based on a modified desired operating point, and operating the equipment in accordance with the first nearest operating point and the second nearest operating point.

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Wauwatosa, WI, United States of America

John H Burroughs