Abstract: Systems and methods executable by a building management system for controlling air cleanliness in a building space are provided. An exemplary method includes predicting an occupancy of the building space during a future time period, identifying an occupant predicted to occupy the building space during the future time period based on the predicted occupancy, obtaining an occupant-specific air cleanliness requirement for the identified occupant, using a predictive air cleanliness model for the building space and the occupant-specific air cleanliness requirement to generate control parameters for building equipment that operate to affect the air cleanliness in the building space, and operating the building equipment using the control parameters to affect the air cleanliness in the building space during the future time period.

Abstract: A method for controlling building equipment includes generating building data relating to conditions in a building space using a generative artificial intelligence model. The method also includes determining whether the building data correspond to conditions in the building space that comply with one or more regulations or certification standards. The method includes, in response to determining that the building data correspond to conditions in the building space that comply with the one or more regulations or certification standards, using the building data to operate building equipment that serve the building space.

Abstract: A method for controlling building equipment includes generating building data relating to conditions in a building space using a generative artificial intelligence model. The method also includes determining whether the building data correspond to conditions in the building space that comply with one or more regulations or certification standards. The method includes, in response to determining that the building data correspond to conditions in the building space that comply with the one or more regulations or certification standards, using the building data to operate building equipment that serve the building space.

Abstract: A heating, ventilation, or air conditioning system (HVAC) design and operational tool includes one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including obtaining a dynamic temperature model and a dynamic infectious quanta model for one or more building zones, determining an infection probability, and performing a plurality of simulations for a plurality of different equipment configurations using the dynamic temperature model, the dynamic infectious quanta model, and the infection probability to generate results. The operations include generating, using the results of the plurality of simulations, at least one of design including one or more recommended design parameters data or operational data including one or more recommended operational parameters for the HVAC system and initiating an automated action using at least one of the design data or the operational data.

Abstract: Methods, systems, and machine-readable storage media for building standard compliance. One system includes a controller including one or more processors configured to receive an activation signal corresponding to an infection control mode (ICM). The one or more processors are further configured to initiate the ICM including activating one or more interventions, wherein the one or more interventions include operating HVAC equipment of the building to meet ICM requirements maintaining an environment corresponding to one or more infection control standards. The one or more processors are further configured to determine an infection risk score based on a performance of the HVAC equipment of the building based on the one or more infection control standards, wherein the infection risk score corresponds to an analysis of an environmental condition of the building and a target environmental condition based on the one or more infection control standards.

Abstract: A Building Management System (BMS) can include one or more memory devices that can store instructions thereon that, when executed by one or more processors, cause the one or more processors to determine emission impacts for a plurality of pieces of building equipment of a building, generate a plurality of recommendations to adjust the emission impacts for the plurality of pieces of building equipment, determine a first point in time to implement at least one recommendation of the plurality of recommendations, and cause a user interface to be displayed.

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 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 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 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.