Abstract: A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, generate a time correlated data stream for a data point, and generate a time correlated reliability data stream for the data point. The building device data includes a plurality of data samples of the data point. The time correlated data stream includes values of the plurality of data samples of the data point. The time correlated reliability data stream includes a plurality of reliability values time correlated to corresponding values of the plurality of data samples of the data point and indicating reliability of the values of the plurality of data samples of the data point.

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 building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, the building device data comprising a plurality of data samples of a data point and generate a time correlated data stream for the data point, the time correlated data stream comprising values of the plurality of data samples of the data point. The instructions cause the one or more processors to generate a time correlated reliability data stream for the data point, the time correlated reliability data stream comprising a plurality of reliability values indicating reliability of the values of the plurality of data samples of the data point.

Abstract: A building manager includes a communications interface configured to receive information from a smart energy grid. The building manager further includes an integrated control layer configured to receive inputs from and to provide outputs to a plurality of building subsystems. The integrated control layer includes a plurality of control algorithm modules configured to process the inputs and to determine the outputs. The building manager further includes a fault detection and diagnostics layer configured to use statistical analysis on the inputs received from the integrated control layer to detect and diagnose faults. The building manager yet further includes a demand response layer configured to process the information received from the smart energy grid to determine adjustments to the plurality of control algorithms of the integrated control layer.

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 at least one of a level of the one or more characteristics of the first fluid or the one or more characteristics of the second fluid.

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: An energy storage system for a building includes a battery asset configured to store electricity and discharge the stored electricity for use in satisfying a building electric load. The system includes a planning tool configured to identify one or more selected functionalities of the energy storage system and generate a cost function defining a cost of operating the energy storage system over an optimization period. The cost function includes a term for each of the selected functionalities. The planning tool is configured to generate optimization constraints based on the selected functionalities, attributes of the battery asset, and the electric energy load to be satisfied. The planning tool is configured to optimize the cost function to determine optimal power setpoints for the battery asset at each of a plurality of time steps of the optimization period.

Abstract: A method for initiating and automatically improving model-driven operations in a low-data scenario includes creating a regression model using pre-operation data prior to initiating the model-driven operations, using the regression model to initiate and perform the model-driven operations during an operational stage, collecting operational data during the operational stage, creating a first artificial neural network model using the operational data, transitioning from using the regression model to perform the model-driven operations to using the first artificial neural network model to perform the model-driven operations responsive to the operational data satisfying a first sufficiency threshold.

Abstract: A mobile sensor for a building includes a sensor, and a wireless transceiver. The sensor is configured to measure an indoor air quality parameter in an air sample at a current location of the mobile sensor as the mobile sensor transports through the building. The wireless transceiver is configured to transmit the measurement of the indoor air quality parameter and an indication of the current location of the mobile sensor to a building controller.

Abstract: A building management system (BMS) operates a plurality of HVAC devices to affect a physical state or condition and detects a fault condition at a second HVAC device affected by the physical state or condition and having causal relationships with the plurality of HVAC devices such that faulty operation of one or more of the plurality of HVAC devices causes a detectable effect at the second HVAC device. The BMS identifies, based on the fault condition, a predetermined set of system parameters corresponding to the fault condition and comprising a plurality of system parameters of the plurality of HVAC devices that are potential causes of the fault condition. The BMS retrieves operating data for the predetermined set of system parameters and transmits a signal to display a user interface comprising the operating data on a display screen.

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: Systems and methods for training a reinforcement learning (RL) model for HVAC control are disclosed herein. Simulated experience data for the HVAC system is generated or received. The simulated experience data is used to initially train the RL model for HVAC control. The HVAC system operates within a building using the RL model and generates real experience data. A determination may be made to retrain the RL model. The real experience data is used to retrain the RL model. In some embodiments, both the simulated and real experience data are used to retrain the RL model. Experience data may be sampled according to various sampling functions. The RL model may be retrained multiple times over time. The RL model may be retrained less frequently over time as more real experience data is used to train the RL model.

Abstract: A building system for a building. The building system can include one or more memory devices. The one or more memory device can store instructions. The instructions, when executed by one or more processors, can cause the one or more processors to receive, from one or more sensors, air quality measurements associated with one or more spaces of the building, and generate, using the air quality measurements, a productivity score for at least one of the building or at least one space of the one or more spaces of the building. The productivity score can be based on a predicted productivity or effect on productivity of one or more occupants of the building in view of the air quality measurements.

Abstract: A feedforward-feedback controller for integrated temperature and pressure control of a building space includes one or more processing circuits. The one or more processing circuits are configured to generate a supply air flow rate setpoint using a combined feedforward-feedback control process that (i) proactively compensates for a feedforward air flow disturbance in the building space and (ii) reactively compensates for a feedback air pressure error in the building space, generate a supply air temperature setpoint using a predictive temperature model that predicts the supply air temperature setpoint required to achieve a zone temperature setpoint for the building space when supply air is provided to the building space at the supply air flow rate setpoint and the supply air temperature setpoint, and operate building equipment to provide the supply air to the building space at the supply air flow rate setpoint and the supply air temperature setpoint.

Abstract: A system for controlling a flow rate of a fluid through a valve includes a controller. The controller is configured to receive a raw flow rate measurement from a flow rate sensor assembly configured to measure the flow rate. The controller is further configured to apply a flow rate measurement filter to the raw flow rate measurement to generate a filtered flow rate measurement. The controller is further configured to control actuation of an actuator configured to change the flow rate using the filtered flow rate measurement. The controller is configured to automatically adjust the flow rate measurement filter in response to detecting an event that causes stoppage of the actuation of the actuator.

Abstract: A method for initiating and automatically improving model-driven operations in a low-data scenario includes creating a regression model using pre-operation data prior to initiating the model-driven operations, using the regression model to initiate and perform the model-driven operations during an operational stage, collecting operational data during the operational stage, creating a first artificial neural network model using the operational data, transitioning from using the regression model to perform the model-driven operations to using the first artificial neural network model to perform the model-driven operations responsive to the operational data satisfying a first sufficiency threshold.

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 system for controlling air quality of a building space includes HVAC equipment configured to serve the building space, sensors configured to measure a plurality of parameters relating to the building space, and a control system. The control system is configured to receive data from the sensors, determine a feedforward air quality contribution, determine a feedback air quality contribution based on a measured air quality and an air quality setpoint for the building space, combine the feedforward air quality contribution and the feedback air quality contribution to determine a target amount of ventilation or filtration to be provided to the building space by the HVAC equipment, and control the HVAC equipment to provide the target amount of ventilation or filtration.

Abstract: A building system of a building includes an in-zone plume control system located within a zone of the building, the in-zone plume control system including one or more return air inlets comprising openings to receive one or more air plumes generated by one or more users. The in-zone plume control system includes one or more exhaust outlets configured to exhaust the one or more air plumes, a filter, and a fan configured to draw the one or more air plumes in through the one or more return air inlets, through the filter, and out through the one or more exhaust outlets back into the zone, wherein the filter is configured to filter the one or more air plumes to remove particles from the one or more air plumes before returning the one or more air plumes to the zone.

Abstract: Systems and methods for particulate sensing of a building. Some embodiments relate to an air handling unit of an HVAC system of a building. The air handling unit includes ducting, one or more air dampers coupled to the ducting, and an integrated sampling system including piping coupled to at least one of the ducting or the one or more air dampers and configured to collect a plurality of air stream samples, a plurality of integrated sensors configured to measure the air stream sample, wherein a first integrated sensor is configured to measure a first species of the air stream sample, and wherein a second integrated sensor is configured to measure a second species of the air stream sample, and a processing circuit configured to estimate one or more air quality indicators of the air handling unit.