Johnson Controls Tyco IP Holdings Llp

Tyco Fire + Security Gmbh

Johnson Controls Technology Company

Anas W I Alanqar

Michael J Wenzel

Mohammad N ElBsat

Matthew J Ellis

Fang Du

Robert D Turney

John H Burroughs

Juan Esteban Tapiero Bernal

Matthew J Asmus

Andrew J Przybylski

Homero L Noboa

Steven R Vitullo

Ryan C Beaty

Shawn A Schlagenhaft

Michael J Sweeney

Ryan A Baumgartner

Nicolaus P Meyer

A method of controlling a direct evaporative cooling unit includes predicting water consumption and energy consumption of the direct evaporative cooling unit based on possible supply air temperatures of the direct evaporative cooling unit, selecting a target supply air temperature based on an optimization of an objective function, the objective function comprising the water consumption and energy consumption, and controlling the direct evaporative cooling unit in accordance with the target supply air temperature.

Direct evaporative cooling system for data center with fan and water optimization

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 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 method includes finding, in subplant performance data points defined by values of an independent variable and values of a dependent variable, a first point for which a convexity metric of the dependent variable with respect to the independent variable exceeds a threshold, creating a subplant curve by providing a first convex region on a first side of the first point and a second convex region on a second side of the first point, and controlling the campus subplant based on the subplant curve.

Central plant with automatic subplant models

A method includes operating equipment in accordance with a setpoint to affect a measurement for a space during a training period, performing a plurality of assessments of different characteristics of data for a segment of the training period, and training a system model using a set of training data. The data for the segment is included in the set of training data in response to passing the plurality of assessments or excluded from the set of training data in response to failing one or more of the plurality of assessments. The method also includes controlling the equipment using the system model.

Building management system with automatic training data selection for online system identification

A method includes obtaining an optimized setpoint schedule for a time period, identifying a pre-cooling or pre-heating segment of the time period of the optimized setpoint schedule, adjusting the optimized setpoint schedule based on a characteristic of the pre-cooling or pre-heating segment to obtain an adjusted setpoint schedule, and operating the building equipment in accordance with the adjusted setpoint schedule.

Building control system with setpoint injection for online system identification

A controller for equipment that operates to affect a variable state or condition of a building including one or more processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include generating a new predictive model using training data associated with one or more durations selected from a time period and selected to satisfy a set of criteria. The predictive model models system dynamics of the building during the time period. The operations include storing the new predictive model in a database including predictive models that model the system dynamics of the building and include comparing performance of the new predictive model and the predictive models stored by the database to select a particular predictive model for controlling the equipment. The operations include using the particular predictive model to generate and provide control signals to the equipment.

Building control system with adaptive online system identification

A method for automatically adapting a predictive model used to control a heating, ventilation, or air conditioning (HVAC) system in a building to compensate for a detected fault in the HVAC system is shown. The method includes obtaining an indication of the detected fault in the HVAC system or a zone in the building. The method further includes determining a predicted impact of the detected fault on an operational performance of the HVAC system. The method further includes adjusting one or more parameters of the predictive model based on the predicted impact of the detected fault to generate a fault-adapted predictive model. The method further includes operating the HVAC system to control an environmental condition of the building using the fault-adapted predictive model.

Building HVAC system with fault-adaptive model predictive control

An environmental control system for a building including building equipment operable to affect a variable state or condition of the building. The system includes a controller including a processing circuit. The processing circuit can obtain training data relating to operation of the building equipment and can perform a system identification process to identify parameters of a system model using the training data. The processing circuit can augment the system model with a disturbance model and estimate values of a historical heat disturbance in the training data based on the augmented system model. The processing circuit can train one or more heat disturbance models based on the training data and the estimated values. The processing circuit can predict a heat disturbance using the augmented system model along with the one or more heat disturbance models and can control the building equipment based on the predicted heat disturbance.

Building control system with heat load estimation using deterministic and stochastic models

A controller for maintaining occupant comfort in a space of a building. The controller includes processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include obtaining building data and obtaining occupant comfort data. The operations include generating an occupant comfort model relating the building data to a level of occupant comfort within the space based on the building data and the occupant comfort data. The operations include generating time-varying comfort constraint for an environmental condition of the space using the occupant comfort model and include performing a cost optimization of a cost function of operating building equipment over a time duration to determine a setpoint for the building equipment. The operations include operating the building equipment based on the setpoint to affect the variable state or condition of the space.

Building control system with automatic comfort constraint generation

A building control system includes one or more controllers configured to obtain comfort feedback provided by one or more occupants of a building in response to exposing the one or more occupants to a first set of environmental conditions of the building. The one or more controllers are also configured to generate, based on the comfort feedback, one or more thresholds defining a range of values for an environmental condition of the building within which the one or more occupants are predicted to be comfortable, execute a predictive control process subject to one or more constraints based on the one or more thresholds to generate setpoints for building equipment, and operate the building equipment to drive the environmental condition of the building toward the setpoints.

Predictive building control system with neural network based comfort prediction

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

An environmental control system for a building including heating, ventilation, or air conditioning (HVAC) equipment that operates to affect a zone of the building and a controller including a processing circuit. The processing circuit is configured to estimate a thermal resistance between air of the zone and of an external space using values of a temperature of the zone air, a temperature of the external space air, and a heat transfer rate of the HVAC equipment, each value corresponding to a different time step within a time period. The processing circuit is configured to use the thermal resistance, time step specific values of the temperatures, and time step specific values of the heat transfer rate to estimate corresponding values of a heat disturbance. The processing circuit is configured to operate the HVAC equipment using a model-based control technique based on the heat disturbance values.

Building control system with heat disturbance estimation and prediction

A building management system includes a processing circuit configured to perform a system identification process to identify one or more parameters of a system model that predicts a behavior of a building system. The one or more parameters include one or more model parameters and one or more Kalman gain parameters. The system identification process includes identifying the one or more model parameters, generating an initial guess of the one or more Kalman gain parameters based on the training data and results of a simulation that uses the one or more model parameters, and identifying the one or more Kalman gain parameters by initializing a prediction error minimization problem with the initial guess. The building management system also includes a controller configured to control building equipment to affect the behavior of the building system based on predictions of the system model.

Building control system with automated Kalman filter parameter initiation and system identification

An HVAC system for automatically adjusting setpoint boundaries of a space includes building equipment configured to provide heating or cooling to the space to affect an environmental condition of the space and a controller. The controller obtains occupant setpoint adjustment data indicating occupant setpoint increases or occupant setpoint decreases at multiple times during a time interval and partitions the occupant setpoint adjustment data into time period bins based on the multiple times associated with the occupant setpoint adjustment data, each of the time period bins containing occupant setpoint adjustment data characterized by a common time attribute. The controller determines a number of occupant setpoint increases and a number of occupant setpoint decreases indicated by the occupant setpoint adjustment data within each time period bin and adjusts a setpoint boundary of the space based on the number of occupant setpoint increases or the number of occupant setpoint decreases.

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Milwaukee, WI, United States of America

Anas W I Alanqar