Abstract: 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.

Abstract: A method for operating a subplant included in a central plant includes obtaining instruction-based equipment models associated with devices included in the subplant and comprises operating points that define an operation of the devices, generating, for each instruction-based equipment models, a geometric equipment model using the operating points from a particular instruction-based equipment model, the geometric equipment model defining at least one operating domain associated with the particular device, merging geometric equipment models to form a geometric subplant model, the geometric subplant model defining an operation of the subplant comprising devices associated with the geometric equipment models, receiving a desired operating point comprising a load value, determining, relative to the desired operating point, a nearest operating point on the geometric subplant model, setting the nearest operating point on the geometric subplant model as an actual operating point, and operating the subplant at the act

Abstract: Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system generates a regression model of a produced thermal energy load produced by a supply device of the plurality of devices. The system predicts the produced thermal energy load produced by the supply device for a first time period based on the regression model. The system determines a heat capacity of gas or liquid in the loop based on the predicted produced thermal energy load. The system generates a model of mass storage based on the heat capacity. The system predicts an induced thermal energy load during a second time period at a consuming device of the plurality of devices based on the model of the mass storage. The system operates the energy plant according to the predicted induced thermal energy load.

Abstract: A controller for HVAC equipment stores a cascaded model that includes a disturbance model configured to predict a heat disturbance affecting the building zone as a function of one or more exogenous parameters and a physics model configured to predict a temperature of the building zone as a function of the heat disturbance and an amount of heating or cooling provided to the building zone by HVAC equipment. The processing circuit is configured to execute a combined training procedure to determine parameters of the disturbance model and parameters of the physics model, generate control signals for the HVAC equipment using the disturbance model to predict the heat disturbance and applying the heat disturbance as an input to the physics model, and operate the HVAC equipment to provide the heating or cooling to the building zone in accordance with the control signals.

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 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: In one aspect, a system for operations an energy plant obtains thermal energy load allocation data indicating time dependent thermal energy load of the energy plant. The system determines, for a time period, an operating state of the energy plant from a plurality of predefined operating states based on the thermal energy load allocation data. The system determines operating parameters of the energy plant according to the determined operating state. The system operates the energy plant according to the determined operating parameters.

Abstract: 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.

Abstract: A predictive power control system includes a battery configured to store and discharge electric power, a battery power inverter configured to control an amount of the electric power stored or discharged from the battery, and a controller. The controller is configured to predict a power output of a photovoltaic field and use the predicted power output of the photovoltaic field to determine a setpoint for the battery power inverter.

Abstract: 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.

Abstract: 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.

Abstract: A building management system (BMS) for filtering a fluid within a building is shown. The system includes one or more sensors configured to measure one or more characteristics of a first fluid within an air duct of the BMS and measure one or more characteristics of a second fluid after the second fluid has been filtered. The system further includes a pollutant management system configured to receive data from the one or more sensors and control a filtration process. The filtration process selects a filter of a plurality of filters based on a level of the one or more characteristics of the first fluid and the one or more characteristics of the second fluid.

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 building system includes building equipment operable to consume one or more resources and a control system configured to generate, based on a prediction model, predictions of a load on the building equipment or a price of the one or more resources for a plurality of time steps in an optimization period, solve, based on the predictions, an optimization problem to generate control inputs for the equipment that minimize a predicted cost of consuming the resources over the optimization period, control the building equipment to operate in accordance with the control inputs, monitor an error metric that characterizes an error between the predictions and actual values of the at least one of the load on the building equipment or the price of the one or more resources during the optimization period, detect an occurrence of a trigger condition, and in response to detecting the trigger condition, update the prediction model.

Abstract: Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system generates a regression model of a produced thermal energy load produced by a supply device of the plurality of devices. The system predicts the produced thermal energy load produced by the supply device for a first time period based on the regression model. The system determines a heat capacity of gas or liquid in the loop based on the predicted produced thermal energy load. The system generates a model of mass storage based on the heat capacity. The system predicts an induced thermal energy load during a second time period at a consuming device of the plurality of devices based on the model of the mass storage. The system operates the energy plant according to the predicted induced thermal energy load.

Abstract: A building management system for generating a building model for a building and operating building equipment of the building based on the building model. The system includes a processing circuit configured to receive a context, wherein the context includes metadata defining the building model for the building and generate a building model editor interface for viewing and editing the received context, wherein the building model interface includes building elements for the building model, wherein the building elements are based on the received context and represent the building equipment. The processing circuit is configured to receive user edits of the context via the building model interface, wherein the user edits include edits to the building elements, generate an updated context based on the user edits of the context, and deploy the updated context to control environmental conditions of the building with the building equipment based on the updated context.

Abstract: 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.

Abstract: A control system for a central plant having subplants including devices operating to serve energy loads of a building. The system includes a high level optimization module that performs high level optimization of thermal loads subject to constraints to generate subplant load allocations. The control system includes a low level optimization module that performs low level optimization of the subplant load allocations to determine operating states for the devices. The control system includes a constraint modifier that modifies the constraints for the high level optimization module based on equipment schedules. The control system also includes a binary optimization modifier including a pruner module that receives the minimum off schedule to determine adjusted branches and a seeder module that receives the minimum on schedule to determine a starting node for use in binary optimization performed by the low optimization module.

Abstract: A control system includes one or more sensors configured to generate sensor data relating to building equipment. The sensor data includes first sensor data collected during a first time period and second sensor data collected during a second time period. The control system also includes a controller configured to use the first sensor data to generate one or more first model coefficients for a model for the building equipment. The controller is also configured to use the second sensor data to generate one or more second model coefficients for the model, generate a test statistic by comparing the one or more first model coefficients and the one or more second model coefficients, compare the test statistic with a value, and automatically identify a change relating to the building equipment in response to the test statistic exceeding the value.