Abstract: A method for providing filtered air to a zone of a building includes operating an air handler to pass unfiltered air through a filter and output filtered air to the zone of the building. The method includes determining an amount of filtered air provided to the zone of the building by the air handler. The method includes determining if additional air filtration is required to satisfy at least one of a desired amount of clean airflow or a desired reduction of a risk of infection. The method includes activating an in-zone filtration device and operating the in-zone filtration device to draw air from the zone into an inner volume, filter the air, and recirculate the filtered air to the zone of the building in response to determining additional air filtration is required.

Abstract: A method is provided for achieving a net energy goal for building operations for a time period including a first subperiod before a current time and a second subperiod from the current time to an end of the time period. The method includes generating first forecasted ranges for amounts of energy consumption for a plurality of time steps in the second subperiod, generating second forecasted ranges for amounts of energy production for the plurality of time steps in the second subperiod, and generating third forecasted ranges for amounts of net energy for the plurality of time steps in the second subperiod. The amounts of net energy are based on differences between the amounts of energy consumption and the amounts of energy production.

Abstract: A building heating, ventilating, or air conditioning (HVAC) system includes waterside HVAC equipment configured to generate a heated or chilled fluid and airside HVAC equipment configured to use the heated or chilled fluid to heat or cool a supply airflow provided to a building. A controller for the HVAC system includes one or more processing circuits configured to perform an integrated airside/waterside control process to determine control outputs for both the waterside HVAC equipment and the airside HVAC equipment simultaneously such that the control outputs for the airside HVAC equipment are based on the control outputs for the waterside HVAC equipment and vice versa. The controller is configured to operate the waterside HVAC equipment and the airside HVAC equipment to provide heating or cooling to the building using the control outputs.

Abstract: A heating, ventilation, or air conditioning (HVAC) system for one or more building zones includes airside HVAC equipment that provide air to the one or more building zones and a controller. The controller obtains predictive models to predict values of a first control objective and a second control objective for the one or more building zones as a function of control decision variables for the airside HVAC equipment. The controller performs a Pareto optimization for a time period using the one or more predictive models to determine multiple sets of Pareto optimal values of the control decision variables and corresponding sets of Pareto optimal values of the first control objective and the second control objective for the time period. The controller operates the airside HVAC equipment to provide the air to the one or more building zones in accordance with a selected set of the Pareto optimal values.

Abstract: A method for training a fault probability model using warranty claim data includes obtaining, by a processing circuit, a first data set for failed building devices based on warranty claim data associated with the building devices; receiving, by the processing circuit, design change data associated with the building devices and determining a design change date based on the design change data; comparing, by the processing circuit, a manufacturing date for each of the failed building devices with the design change date; removing, by the processing circuit, any building devices from the first data set in response to the manufacturing date preceding the design change date to create an updated first data set; generating, by the processing circuit, a training data set comprising the updated first data set; and training, by the processing circuit, a fault probability model using the training data set to produce a trained model.

Abstract: A controller for heating, ventilation, or air conditioning (HVAC) equipment operable to affect an environmental condition of a building is configured to obtain predictive models that predict values of an energy control objective and an air quality control objective as a function of control decision variables for the HVAC equipment. The controller executes a multi-objective optimization process using the predictive models to produce multiple sets of optimization results corresponding to different values of the control decision variables, the energy control objective, and the air quality control objective. The controller selects one or more of the sets of optimization results based on the values of the energy control objective and the air quality control objective. The controller operates the HVAC equipment to affect the environmental condition of the building in accordance with the values of the control decision variables corresponding to a selected set of the optimization results.

Abstract: A controller for heating, ventilation, or air conditioning (HVAC) equipment that is operable to affect an environmental condition of a building is configured to obtain predictive models that predict values of a carbon emissions control objective and another control objective as a function of control decision variables for the HVAC equipment. The controller executes an optimization process using the predictive models to produce sets of optimization results corresponding to different values of the control decision variables, the carbon emissions control objective, and the other control objective. The controller selects from the sets of optimization results based on the values of the carbon emissions control objective and the other control objective. The controller operates the HVAC equipment to affect the environmental condition of the building in accordance with the values of the control decision variables corresponding to a selected set of the optimization results.

Abstract: A controller for heating, ventilation, or air conditioning (HVAC) equipment including a processing circuit configured to perform a first optimization to generate a first set of control decisions for HVAC equipment including waterside HVAC equipment that consume resources from utility providers to generate a heated or chilled fluid and airside HVAC equipment that receive and use the fluid from the waterside HVAC equipment to heat or cool a supply of airflow for a building. The processing circuit is configured to perform a second optimization subject to a constraint based on a result of the first optimization to generate a second set of control decisions for the HVAC equipment and to combine the first and second sets to generate a combined set of control decisions for the HVAC equipment. The processing circuit is configured to operate the HVAC equipment in accordance with the combined set of control decisions.

Abstract: A method for providing filtered air to a zone of a building includes operating an air handler to pass unfiltered air through a filter and output filtered air to the zone of the building. The method includes determining an amount of filtered air provided to the zone of the building by the air handler. The method includes determining if additional air filtration is required to satisfy at least one of a desired amount of clean airflow or a desired reduction of a risk of infection. The method includes activating an in-zone filtration device and operating the in-zone filtration device to draw air from the zone into an inner volume, filter the air, and recirculate the filtered air to the zone of the building in response to determining additional air filtration is required.

Abstract: A method includes automatically selecting a prediction horizon used by the predictive model by performing evaluations of model performance at successively narrower ranges of possible prediction horizons until the prediction horizon is determined based on results of the evaluations. The method may also include using the predictive model with the prediction horizon to perform an automated control action, which may include at least one of controlling or monitoring the building equipment.

Abstract: A method includes training a conditional generator by operating a generative adversarial network that includes the conditional generator, generating, by the conditional generator, synthetic timeseries data corresponding to a plurality of fault types, wherein labels for the plurality of fault types are used as inputs to the conditional generator, training a fault prediction model using the synthetic timeseries data, and predicting a fault for building equipment by applying the fault prediction model to real timeseries data relating to the building equipment.

Abstract: A system includes a plurality of devices of building equipment, an additional device of building equipment, and a computing system. The computing system is configured to process data from the plurality of devices to extract common features of the plurality of devices, train a global model based on the common features, obtain additional data from the additional device, adapt the global model for the additional device based on the additional data to obtain an adapted model for the additional device, predict a status of the additional device using the adapted model, and affect an operation of the additional device based on the status.

Abstract: A method includes obtaining a fault prediction model for building equipment, predicting, with the fault prediction model, both (i) whether a fault will occur during a first prediction bin and (ii) whether a fault will occur during a second prediction bin, performing a first mitigating action for the building equipment if the fault is predicted to occur during the first prediction bin, and performing a second mitigating action for the building equipment if the fault is predicted to occur during the second prediction bin.

Abstract: Systems and methods for executing an IAQ analysis of a building. One system includes a controller including memory and one or more processors configured to obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, obtain BAS data, identify one or more unknown parameters from the IAQ data and BAS data of two or more of the plurality of environment species, estimate the one or more unknown parameters based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the plurality of environment species subject to the two or more of the plurality of environment species evolving according to a single-species concentration model, and provide the estimated one or more unknown parameters to one or more predictive models.

Abstract: Systems and methods for executing an IAQ analysis of a building. One system includes a controller including memory and one or more processors configured to obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, obtain BAS data, identify one or more unknown parameters from the IAQ data and BAS data of two or more of the plurality of environment species, estimate the one or more unknown parameters based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the plurality of environment species subject to the two or more of the plurality of environment species evolving according to a single-species concentration model, and provide the estimated one or more unknown parameters to one or more predictive models.

Abstract: Systems and methods for controlling building equipment based on an indoor air quality (IAQ) ventilation analysis of a building. One system includes a controller including memory and one or more processors configured to continuously collect IAQ data from one or more sensors within the building, estimate a plurality of outdoor airflow rates for an area of the building during a plurality of transient periods using the IAQ data as input, generate a time series outdoor airflow rate includes the plurality of estimated outdoor airflow rates, and modify a control strategy for the area of the building based on the time series outdoor airflow rate and a ventilation schedule for the area of the building.

Abstract: A method includes providing a net consumption trajectory comprising net consumption targets for one or more subperiods of a time period. Each net consumption target indicates a target difference from a beginning of a time period to an end of the subperiod between total consumption and total production or offset. The method also includes generating, for a subperiod of the plurality of subperiods, a set of curtailment actions predicted to achieve the net consumption target for the subperiod and implementing the set of curtailment actions.

Abstract: A building system can operate to receive a requirement to implement an artificial intelligence (AI) service. The requirement can include an indication of a type of an entity, wherein the AI service is configured to generate an analytic for the entity of the type of the entity or a control setting for the entity of the type of the entity. The requirement can include a data element that the AI service is configured to operate on to generate the analytic or the control setting. The building system can operate to determine that the building system meets the requirement to implement the AI service responsive to a determination that a digital twin of the building system includes the entity of the entity type and the data element and implement the AI service responsive to a determination that the building system meets the requirement.

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: Systems and methods for controlling building equipment include simulating operation of the building equipment at a plurality of initial points to generate corresponding values of a first control objective and a second control objective that competes with the first control objective, automatically generating a new point at which to run a new simulation based on the plurality of initial points and the corresponding values of the control objectives, running the new simulation at the new point to generate corresponding values of the control objectives, classifying a subset of the plurality of initial points and the new point as Pareto-optimal points based on the corresponding values of the control objectives, and operating the building equipment at a Pareto-optimal point selected from the subset of the plurality of initial points and the new point classified as Pareto-optimal points.