Abstract: A building cooling system includes one or more cooling devices operable to affect an indoor air temperature of a building and a system management circuit. The system management circuit is configured to obtain an objective function that includes a power consumption term and a comfort term, perform an optimization of the objective function over a time horizon to determine values of the cooling capacity of the cooling devices where each value of the cooling capacity corresponds to a time step of the time horizon, and control the cooling devices based on the values of the cooling capacity of the cooling devices. The comfort term of the objective function a difference between a prediction of the indoor air temperature of the building and a temperature setpoint for the building, while the power consumption term is a function of the power consumption of the one or more cooling devices.

Abstract: A building energy system includes a controller configured to obtain representative loads and rates for a plurality of scenarios and generate a cost function comprising a risk attribute and multiple demand charges. Each of the demand charges corresponds to a demand charge period and defines a cost based on a maximum amount of at least one of the energy resources purchased within the corresponding demand charge period. The controller is configured to determine, for each of the multiple demand charges, a peak demand target for the corresponding demand charge period by performing a first optimization of the risk attribute over the plurality of the scenarios, allocate an amount of the one or more energy resources to be consumed, produced, stored, or discharged by the building equipment by performing a second optimization subject to one or more constraints based on the peak demand target for each of the multiple demand charges.

Abstract: A building management system includes HVAC equipment operable to affect an indoor air temperature of a building, a system manager configured to obtain a cost function that characterizes a cost of operating the HVAC equipment, obtain a dataset relating to the building, determine a current state of the building by applying the dataset to a neural network, select a temperature bound associated with the current state, augment the cost function to include a penalty term that increases the cost when the indoor air temperature violates the temperature bound, and determine a temperature setpoint for each of a plurality of time steps in the future time period. The temperature setpoints achieve a target value of the cost function over the future time period. The building management system also includes a controller configured to operate the HVAC equipment to drive the indoor air temperature towards the temperature setpoint.

Abstract: 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.

Abstract: A building cooling system includes a controller and a cooling device operable to affect indoor air temperature of a building. The controller is configured to obtain a cost function that characterizes a cost of operating the cooling device over a future time period, obtain a dataset relating to the building, determine a current state of the building by applying the dataset to a neural network, select a temperature bound associated with the current state, augment the cost function to include a penalty term that increases the cost when the indoor air temperature violates the temperature bound, and determine a temperature setpoint for each of a plurality of time steps in the future time period. The temperature setpoints achieve a target value of the cost function over the future time period. The controller is configured to control the cooling device to drive the indoor air temperature towards the temperature setpoint.

Abstract: 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.

Abstract: A refrigeration cycle, an air conditioning system, and a method for controlling a refrigeration cycle are provided. The refrigeration cycle includes an outdoor unit, an indoor unit, a controller, and an inverter. The controller controls a compressor and an outdoor fan of the air conditioning system so as to minimize a total electric power consumption of the air conditioning system. The inverter controls the outdoor fan in a rotation state predicted from a capacity demand in an air conditioning space depending on an operation mode and sensor values. The controller predicts the capacity demand and controls a rotation rate of the outdoor fan based on a prediction of the capacity demand.

Abstract: A building energy system includes HVAC equipment, green energy generation, a battery, and a predictive controller. The HVAC equipment provide heating or cooling for a building. The green energy generation collect green energy from a green energy source. The battery stores electric energy including at least a portion of the green energy provided by the green energy generation and grid energy purchased from an energy grid and discharges the stored electric energy for use in powering the HVAC equipment. The predictive controller generates a constraint that defines a total energy consumption of the HVAC equipment at each time step of an optimization period as a summation of multiple source-specific energy components and optimizes the predictive cost function subject to the constraint to determine values for each of the source-specific energy components at each time step of the optimization period.

Abstract: An air conditioner controller for an air conditioner is configured to obtain a first value of a performance value from a predetermined component of the air conditioner, and estimate a total cost. The controller is configured to estimate the total cost including an operation cost and a renewal cost of the air conditioner based on the first value of the performance variable obtained from the predetermined component of the air conditioner and a second value of the performance variable estimated by an operational model of the air conditioner. The total cost is a total cost of operating and renewing the air conditioner for a time period after a time at which the first value of the performance variable is obtained. The controller is configured to output the total cost via a user interface.

Abstract: A building control system includes one or more processors and one or more non-transitory computer-readable media storing instructions. When executed by the one or more processors, the instructions cause the one or more processors to perform operations including using an artificial intelligence model to adjust a threshold value of a constraint based on user input provided via one or more user devices during a first time period. The user input indicates user satisfaction with an environmental condition of a building space during the first time period. The operations include using the threshold value of the constraint to operate equipment that affect the environmental condition of the building space during a second time period subsequent to the first time period.

Abstract: A method for controlling a variable refrigerant flow (VRF) system includes applying a time window to sensor data associated with the VRF system, the sensor data including input data points and having a first resolution, wherein applying the time window to the sensor data isolates a subset of the input data points; applying a timing weight to one or more input data points in the subset of the input data points to generate corrected data points having a second resolution higher than the first resolution; creating a virtual sensor and mapping the corrected data points to an output of the virtual sensor; and controlling the VRF system based on an output of the virtual sensor. The use of virtual sensors with a higher resolution than corresponding physical sensors in this manner allows for existing physical sensors to be used while improving performance of the VRF system.

Abstract: A controller for equipment obtains utility rate data indicating a price of one or more resources consumed by the equipment to serve energy loads. The controller generates 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 one or more resources consumed by the equipment at each of a plurality of time steps. The controller optimizes the objective function to determine a distribution of predicted energy loads across the equipment at each of the plurality of time steps. Load equality constraints on the objective function ensure that the distribution satisfies the predicted energy loads at each of the plurality of time steps. The controller operates the equipment to achieve the distribution of the predicted energy loads at each of the plurality of time steps.

Abstract: A heating, ventilation, or air conditioning (HVAC) system for a building includes airside HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers are configured to generate airside energy targets for the one or more building spaces using a heat transfer model that defines a relationship between the airside energy targets, a temperature of the one or more building spaces, and a thermal capacitance of the one or more building spaces. The one or more controllers are configured to control the airside HVAC equipment in accordance with the airside energy targets.

Abstract: A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.

Abstract: An energy storage system includes a battery and an energy storage controller. The battery is configured to store electrical energy purchased from a utility and to discharge the stored electrical energy for use in satisfying a building energy load. The energy storage controller is configured to generate a cost function including multiple demand charges. Each of the demand charges corresponds to a demand charge period and defines a cost based on a maximum amount of the electrical energy purchased from the utility during any time step within the corresponding demand charge period. The controller is configured to modify the cost function by applying a demand charge mask to each of the multiple demand charges. The demand charge masks cause the controller to disregard the electrical energy purchased from the utility during any time steps that occur outside the corresponding demand charge period when calculating a value for the demand charge.

Abstract: A variable refrigerant flow (VRF) system for a building includes a plurality of outdoor VRF units configured to heat or cool a refrigerant for use in heating or cooling the building and an extremum-seeking controller. The extremum-seeking controller is configured to determine a total power consumption of the plurality of outdoor VRF units, generate a pressure setpoint for the plurality of outdoor VRF units using an extremum-seeking control technique that drives the total power consumption toward an extremum, and use the pressure setpoint to operate the plurality of outdoor VRF units.

Abstract: 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.

Abstract: 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.

Abstract: A predictive heating system for a building zone includes building equipment, a temperature sensor, a humidity sensor, and a predictive heating controller. The building equipment is operable to affect an environmental condition of the building zone in a heating mode of operation and a cooling mode of operation. The temperature sensor is configured to measure a temperature of the building zone. The humidity sensor is configured to measure humidify of the building zone. The predictive heating controller is configured to predict an occupancy time of the building zone over a future time period, determine a dehumidification time period before the occupancy time of the building zone, determine a heating time period before the occupancy time of the building zone, operate the building equipment to dehumidify the building zone over the dehumidification time period, and operate the building equipment to heat the building zone over the heating time period.

Abstract: A predictive building control system includes equipment operable to provide heating or cooling to a building and a predictive controller. The predictive controller includes one or more optimization controllers configured to perform an optimization to generate setpoints for the equipment at each time step of an optimization period subject to one or more constraints, a constraint generator configured to use a neural network model to generate the one or more constraints, and an equipment controller configured to operate the equipment to achieve the setpoints generated by the one or more optimization controllers at each time step of the optimization period.