Johnson Controls Technology Company

Johnson Controls Tyco IP Holdings Llp

Tyco Fire + Security Gmbh

Juan Esteban Tapiero Bernal

Matthew J Ellis

Michael J Wenzel

Mohammad N ElBsat

Robert D Turney

Brennan H Fentzlaff

Anas W I Alanqar

John H Burroughs

Alan S Schwegler

Kerry M Bell

Leyla Mousavi

Bridget E Kapler

Kierstyn R Robbins

Kirk H Drees

John Ismael Ruiz

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 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

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.

Building management system with automatic comfort constraint adjustment

A building management system includes a controller configured to control building equipment by providing a control input to the building equipment for each of the plurality of time steps and generate a set of training data for a system model for the building. The training data includes input training data and output training data for each of the plurality of time steps. The controller is further configured to perform a system identification process to identify parameters of the system model. The system identification process includes predicting, for each time step, a predicted value for one or more of the output variables for each of a plurality of subsequent time steps, generating a prediction error function by comparing the output training data to the predicted values, and optimizing the prediction error function to determine values for the parameters of the system model that minimize the prediction error function.

Building management system with system identification using multi-step ahead error prediction

A building management system includes building equipment operable generate training data relating to behavior of a building system and a controller configured to perform a system identification process that includes generating a prediction error function based on the training data and a system model, generating initial guesses of one or more parameters of the system model, running an optimization problem of the prediction error function for a first group of iterations, discarding, after the first group of iterations, a portion of the initial guesses based on one or more criteria and ranking a remaining portion of the initial guesses, running the optimization problem of the prediction error function for a top-ranked initial guess of the remaining portion to local optimality to identify a first set of values of the one or more parameters, and identifying the one or more parameters as having the first set of values.

Building management system with efficient model generation for system identification

An energy storage system includes a photovoltaic energy field, a stationary energy storage device, an energy converter, and a controller. The photovoltaic energy field converts solar energy into electrical energy and charges the stationary energy storage device with the electrical energy. The energy converter converts the electrical energy stored in the stationary energy storage device into AC power at a discharge rate and supplies a campus with the AC power at the discharge rate. The controller generates a cost function of the energy consumption of the campus across a time horizon which relates a cost to operate the campus to the discharge rate of the AC power supplied by the stationary energy storage device. The controller applies constraints to the cost function, determines a minimizing solution to the cost function which satisfies the constraints, and controls the energy converter.

Photovoltaic energy system with stationary energy storage control

A thermostat includes an equipment controller and a model predictive controller. The equipment controller is configured to drive the temperature of a building zone to an optimal temperature setpoint by operating HVAC equipment to provide heating or cooling to the building zone. The model predictive controller is configured to determine the optimal temperature setpoint by generating a cost function that accounts for a cost operating the HVAC equipment during each of a plurality of time steps in an optimization period, using a predictive model to predict the temperature of the building zone during each of the plurality of time steps, and optimizing the cost function subject to a constraint on the predicted temperature of the building zone to determine optimal temperature setpoints for each of the time steps.

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Wauwatosa, WI, United States of America

Juan Esteban Tapiero Bernal