Abstract: An air handling unit (AHU) or rooftop unit (RTU) or other building device in a building includes one or more powered components and is used with a battery, and a predictive controller The battery is configured to store electric energy and discharge the stored electric energy for use in powering the powered components. The predictive controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from an energy grid and an optimal amount of electric energy to store in the battery or discharge from the battery for use in powering the powered components at each time step of an optimization period.

Abstract: A method includes determining control setpoints for equipment based on a time-varying availability of green energy and revenue from an incentive program of an energy provider. The method also includes controlling the equipment using the control setpoints.

Abstract: 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.

Abstract: 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.

Abstract: A building management system includes building equipment configured to consume electrical energy and generate thermal energy, thermal energy storage configured to store at least a portion of the thermal energy generated by the building equipment and to discharge the stored thermal energy, electrical energy storage configured to store electrical energy purchased from a utility and to discharge the stored electrical energy, and a controller. The controller is configured to determine, for each time step within a time horizon, an optimal amount of electrical energy stored or discharged by the electrical energy storage by optimizing a value function.

Abstract: 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.

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 system for allocating one or more resources including electrical energy across equipment that operate to satisfy a resource demand of a building. The system includes electrical energy storage including one or more batteries configured to store electrical energy purchased from a utility and to discharge the stored electrical energy. The system further includes a controller configured to determine an allocation of the one or more resources by performing an optimization of a value function. The value function includes a monetized cost of capacity loss for the electrical energy storage predicted to result from battery degradation due to a potential allocation of the one or more resources. The controller is further configured to use the allocation of the one or more resources to operate the electrical energy storage.

Abstract: A model predictive maintenance system for building equipment including an equipment controller to operate the building equipment to affect a variable state or condition in a building. The system includes an operational cost predictor to predict a cost of operating the building equipment over a duration of an optimization period, a maintenance cost predictor to predict a cost of performing maintenance on the building equipment, and a cost incentive manager to determine whether any cost incentives are available and, in response to a determination that cost incentives are available, identify the cost incentives. The system includes an objective function optimizer 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, the predicted cost of performing maintenance on the building equipment, and, if available, the cost incentives.

Abstract: A heating, ventilation, or air conditioning (HVAC) system for a building includes one or more processing circuits having one or more processors and one or more non-transitory computer-readable media containing program instructions. When executed by the one or more processors, the instructions cause the one or more processors to perform operations including providing an optimization function for operating HVAC equipment over a future time period including a plurality of time steps and using the optimization function to generate a time series of temperature setpoints for the plurality of time steps in the future time period. The time series of temperature setpoints achieve a target value of the optimization function over the future time period. The operations include operating the HVAC equipment to drive indoor air temperature toward a first temperature setpoint of the time series of temperature setpoints for a first time step of the plurality of time steps.

Abstract: A controller for a building system receives training data including input data and output data. The output data indicate a state of the building system affected by the input data. The controller pre-processes the training data using a first set of pre-processing options to generate a first set of training data and pre-processes the training data using a second set of pre-processing options to generate a second set of training data. The controller performs a multi-stage optimization process to identify multiple different sets of model parameters of a dynamic model for the building system. The multi-stage optimization process includes a first stage in which the controller uses the first set of training data to identify a first set of model parameters and a second stage in which the controller uses the second set of training data to identify a second set of model parameters. The controller uses the dynamic model to operate the building system.

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 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 building HVAC system includes an airside system having a plurality of airside subsystems, a high-level model predictive controller (MPC), and a plurality of low-level airside MPCs. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to the airside subsystem. The high-level MPC is configured to perform a high-level optimization to generate an optimal airside subsystem load profile for each airside subsystem. The optimal airside subsystem load profiles optimize energy cost. Each of the low-level airside MPCs corresponds to one of the airside subsystems and is configured to perform a low-level optimization to generate optimal airside temperature setpoints for the corresponding airside subsystem using the optimal airside subsystem load profile for the corresponding airside subsystem.

Abstract: A controller for performing a peer analysis of a predictive model for a building including processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include comparing model parameters of a set of predictive models corresponding to a set of building zones, the model parameters indicating system dynamics of the building zones, to determine whether any of the building zones are exhibiting abnormal system dynamics. The operations include initiating a corrective action in response to determining that at least one of the building zones is exhibiting abnormal system dynamics.

Abstract: 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.

Abstract: A building HVAC system includes an airside system having a plurality of airside subsystems, a high-level model predictive controller (MPC), and a plurality of low-level airside MPCs. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to the airside subsystem. The high-level MPC is configured to perform a high-level optimization to generate an optimal airside subsystem load profile for each airside subsystem. The optimal airside subsystem load profiles optimize energy cost. Each of the low-level airside MPCs corresponds to one of the airside subsystems and is configured to perform a low-level optimization to generate optimal airside temperature setpoints for the corresponding airside subsystem using the optimal airside subsystem load profile for the corresponding airside subsystem.

Abstract: An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.

Abstract: A smart edge controller for building equipment that operates to affect a variable state or condition within 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 including obtaining sensor data indicating environmental conditions of the building and include determining an amount of available processing resources at the smart edge controller or at the building equipment. The operations include automatically scaling a level of complexity of an optimization of a cost function based on the available processing resources and include performing the optimization of the cost function at the automatically scaled level of complexity to generate a first setpoint trajectory. The first setpoint trajectory includes operating setpoints for the building equipment at time steps within an optimization period.

Abstract: A building optimization system includes charging and discharging a battery of a battery power vehicle. The building optimization system includes a charging system configured to cause the battery of the battery powered vehicle to charge or discharge. The building optimization system also includes an optimization controller including a processing circuit. The processing circuit is configured to receive charging constraints for the battery powered vehicle, determine whether to charge discharge the battery of the battery powered vehicle based on the charging constraints, and cause the charging system to charge or discharge the battery of the battery powered vehicle based on the optimization.