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 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: A building system for a building including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to operate one or more pieces of building equipment associated with a building space based on a first operating mode. The instructions cause the one or more processors to receive an indication to update operation of the building space based on an emergency situation and responsive to receiving the indication to update operation of the building space based on the emergency situation, operate the one or more pieces of building equipment based on a second operating mode, wherein the second operating mode defines one or more second parameters for the one or more pieces of building equipment and is adapted to reconfigure the operation of the building space to address or mitigate the emergency situation.

Abstract: A system includes a processing circuit configured to provide an energy use setpoint based on energy data including an energy characteristic and subject to a constraint based on a variable condition of a building. The processing circuit is configured to estimate a value for the energy use setpoint that will result in the variable condition of the building satisfying the constraint and provide a control signal for equipment based on a difference between the energy use setpoint and a measured energy use.

Abstract: An actuator for controlling a flow regulation device. The actuator includes a drive device coupled to the flow regulation device and a motor coupled to the drive device and operable to move the drive device. The actuator further includes a processing circuit configured to receive a first measurement of a characteristic, control the actuator to reposition the flow regulation device in a first direction, receive a second measurement of the characteristic, determine an installation error associated with at least one of the actuator and the flow regulation device based on the first measurement and the second measurement.

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 method for controlling flow in a heating, ventilation, and air conditioning (HVAC) system includes adjusting a setpoint associated with a valve based on a temperature change across a heating or cooling coil to reduce energy waste. The method includes receiving a first temperature measurement associated with an inlet of the coil, receiving a second temperature measurement associated with an outlet of the coil, calculating a difference between the first temperature measurement and the second temperature measurement, determining that the difference between the first temperature measurement and the second temperature measurement is below a threshold, and adjusting a setpoint associated with the valve. Additional control features may be provided to improve system behavior such as a pulse generation feature, a change-limiting feature, and a reevaluation feature.

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 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: A cascaded control system is configured to control power consumption of a building during a demand limiting period. The cascaded control system includes an energy use setpoint generator and a feedback controller. The energy use setpoint generator is configured to use energy pricing data and measurements of a variable condition within the building to generate an energy use setpoint during the demand limiting period. The feedback controller is configured to use a difference between the energy use setpoint and a measured energy use to generate a control signal for building equipment that operate to affect the variable condition within the building during the demand limiting period.

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: A method for controlling flow in a heating, ventilation, and air conditioning (HVAC) system that imposes an upper limit on the flow of fluid through a heating or cooling coil. Imposing this limit on the flow rate ensures that a temperature change across the coil remains above a minimum threshold and can significantly reduce energy waste. The method includes receiving a first temperature measurement associated with an inlet of the coil, receiving a second temperature measurement associated with an outlet of the coil, and receiving a flow measurement associated with the valve, applying the first temperature measurement, the second temperature measurement, and the flow measurement as input to a model, determining a maximum flow rate that ensures that a difference between the first temperature measurement and the second temperature measurement is above a threshold using the model, and operating the valve in accordance with the maximum flow rate.

Abstract: An electrical energy storage system includes a battery container, one or more temperature sensors, and a controller. The battery container includes one or more batteries configured to store and discharge electrical energy. The temperature sensors are configured to measure one or more temperatures associated with the battery container. The controller is configured to estimate a rate of heat generation by the one or more batteries based on the measured temperatures, monitor an electric current provided to the one or more batteries, and estimate a resistance of the one or more batteries based on the estimated rate of heat generation and the electric current.

Abstract: A method of predicting a time of effect of an intervention of a point of a Building Management System (BMS). The method includes evaluating a first input to determine how an intervention of a point will affect a variable of the BMS; predicting a time at which the intervention will affect the variable of the BMS; presenting feedback to a user via a user interface before implementing the intervention of the point, the feedback comprising the time at which the intervention of the point is predicted to affect the variable; and implementing the intervention or a cancellation of the intervention based at least in part on a second input from the user or an automated response to the feedback. The method allows for users to determine whether to implement proposed interventions in real-time.

Abstract: An adaptive flow limit controller for controlling a flow rate of a fluid through a heat exchanger includes a processing circuit. The processing circuit is configured to use an adaptive model to determine a threshold flow rate of the fluid through the heat exchanger at which a gradient of a temperature difference of the fluid across the heat exchanger with respect to the flow rate of the fluid through the heat exchanger has a threshold gradient value. The processing circuit is configured to operate a flow control device to maintain the flow rate of the fluid of through the heat exchanger at less than or equal to the threshold flow rate.

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 method for calibrating a flow sensor in a heating, ventilation, or air conditioning (HVAC) system. The method includes receiving, at a controller, a request to enter a calibration mode and, in response to receiving the request, automatically commanding a flow control device to achieve a target flow rate. The method further includes generating, by the controller, calibration data for the flow sensor using a reference flow value of the flow rate when the flow control device has achieved the target flow rate and a corresponding flow measurement from the flow sensor.

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: Systems and methods for training a reinforcement learning (RL) model for HVAC control are disclosed herein. A calibrated simulation model is used to train a surrogate model of the HVAC system operating within a building. The surrogate model is used to generate simulated experience data for the HVAC system. The simulated experience data can be used to train a reinforcement learning (RL) model of the HVAC system. The RL model is used to control the HVAC system based on the current state of the system and the best predicted action to perform in the current state. The HVAC system generates real experience data based on the actual operation of the HVAC system within the building. The real experience data is used to retrain the surrogate model, and additional simulated experience data is generated using the surrogate model. The RL model can be retrained using the additional simulated experience data.

Abstract: A building management system is configured to modify an environmental condition of a building. The building management system includes a valve assembly, an actuator device, and a network sensor device. The actuator device includes a motor, a drive device driven by the motor and coupled to the valve assembly, and a controller coupled to the motor. The controller includes a microprocessor and a control application configured to enable closed loop control of the valve assembly. The network sensor device is communicably coupled to the actuator device and configured to measure an environmental property. The control application is configured to perform the closed loop control of the valve assembly based on an input control signal from a mobile device and the measured environmental property from the network sensor device.